Den här inlämningsuppgiften förutsätter att följande paket finns installerade:
mosaic
dplyr
geosphere
leaflet
Paket kan installeras via kommandot install.packages('packagename'), där 'packagename' är namnet på paketet, t.ex 'mosaic'.
Introduktion
I den första inlämningsuppgiften ska ni självständigt i grupper om tre analysera ett dataset i programmeringsspråket R. Till skillnad från datorlaborationerna finns det minimalt med kodexempel. Datorlaborationerna går igenom de flesta momenten som behandlas i inlämningsuppgiften, så se till att göra klart dessa innan.
I denna inlämningsuppgift ska ni analysera ett datamaterial som beskriver ca 500 distrikt i Boston år 1970. Datasetet är en modifierad version av originaldata som användes i en studie där författarna predikterade medianhuspriser i olika distrikt med hjälp av en rad förklaringsvariabler.
Följande variabler finns i datasetet boston_census_data.Rdata (ladda ner ), som innehåller 480 observationer. Notera att en observation motsvarar ett distrikt:
town: Stadsdel.longitude: Longitud koordinat.latitude: Latitud koordinat.median_home_value: Medianhuspriset (enhet 1K USD).crime_rate: Brott (per 1000 invånare).zoned_25k_p: Andel av stadsdelens bostadsmark ämnad för marklotter större än 25000 kvadratfot.indust_p: Andel tunnland ägd av företag utanför detaljhandel.borders_charles: Charles River dummy variabel (= 1 om området gränsar till floden, 0 annars).NOx: Koncentration av kväveoxider (andelar per 10 miljon).n_rooms_avg: Genomsnitt antal rum i ägda bostäder.before_1940_p: Andel ägda bostäder byggda före 1940.employ_dist: Viktat avstånd till fem arbetsförmedlingscentra i Boston.radial_access: Index som mäter tillgång till stadsmotorvägar.tax_rate: Fastighetsskatt per 10000 USD.pupil_teacher_ratio: Lärartäthet mätt som elev per lärare.lower_stat_pct: Procentandel med låg socioekonomisk status i termer av utbildning eller arbete.dist_fenway_park: Avstånd till stadion Fenway Park.
Inlämningsuppgiften ska lämnas in i form av ett html-dokument genererat av Quarto. Kontrollera att ni inte får några felmeddelande när du genererar HTML-dokumentet. Läs sedan igenom HTML-dokumentet noggrant innan ni lämnar in det. Använd tydliga figurer och namnge axlarna med tydliga variabelnamn. Glöm inte att skriva era namn i början av dokumentet, där det nu står Namn 1, Namn 2 och Namn 3.
0. Ladda in data
💪 Uppgift 0.1
Ladda in datasetet Boston_census_data med följande kod.
load (file = url ("https://github.com/StatisticsSU/SDA1/blob/main/assignments/assignment1/Boston_census_data.RData?raw=true" )) <- Boston_census_data
town longitude latitude median_home_value crime_rate
1 Somerville -71.0677 42.2335 17.4 0.32264
2 Weymouth -70.9650 42.1503 18.7 0.06151
3 Boston East Boston -71.0215 42.2270 12.3 7.99248
4 Boston Forest Hills -71.0511 42.1879 13.3 6.39312
5 Boston Savin Hill -71.0455 42.1768 14.4 9.51363
6 Burlington -71.1095 42.3008 21.7 0.15876
7 Boston Savin Hill -71.0400 42.1770 17.8 8.24809
8 Boston Savin Hill -71.0445 42.1938 8.4 13.67810
9 Watertown -71.1050 42.2235 28.7 0.07013
10 Pembroke -70.8530 42.0520 18.2 0.04301
11 Wellesley -71.1660 42.1870 45.4 0.03578
12 Cambridge -71.0480 42.2222 13.4 3.32105
13 Boston Savin Hill -71.0345 42.1850 17.1 9.72418
14 Boston Mattapan -71.0740 42.1680 19.9 4.34879
15 Winthrop -70.9875 42.2240 22.0 0.10959
16 Boston Savin Hill -71.0495 42.1815 14.3 5.58107
17 Lynn -70.9730 42.2790 14.5 0.98843
18 Salem -70.9510 42.3060 21.7 0.09378
19 Boston Allston-Brighton -71.0865 42.2100 19.9 3.83684
20 Boston Downtown -71.0427 42.2090 27.9 11.95110
21 Medford -71.0600 42.2540 19.4 0.26363
22 Dedham -71.0982 42.1445 24.4 0.11460
23 Natick -71.2055 42.1875 23.7 0.08244
24 Cambridge -71.0662 42.2162 17.0 1.41385
25 Marshfield -70.8100 42.0590 18.6 0.07244
26 Norwell -70.9200 42.1016 24.5 0.01501
27 Norwood -71.1309 42.1166 28.6 0.12932
28 Boston Forest Hills -71.0670 42.1945 21.4 14.33370
29 Boston East Boston -71.0200 42.2205 9.7 11.57790
30 Melrose -71.0425 42.2715 22.0 0.03932
31 Peabody -70.9525 42.3208 19.4 0.21977
32 Boston Charlestown -71.0460 42.2300 10.4 88.97620
33 Cambridge -71.0645 42.2150 19.4 2.14918
34 Medford -71.0668 42.2545 27.5 0.14866
35 Boston South Boston -71.0167 42.2010 13.8 8.64476
36 Lynn -70.9720 42.2765 13.2 1.38799
37 Somerville -71.0760 42.2405 15.6 0.97617
38 Boston Forest Hills -71.0732 42.1840 29.8 4.64689
39 Boston Savin Hill -71.0428 42.1845 13.0 7.52601
40 Cambridge -71.0690 42.2285 41.3 1.22358
41 Weymouth -70.9650 42.1010 20.7 0.03738
42 Stoneham -71.0530 42.2830 22.9 0.04203
43 Malden -71.0428 42.2580 18.7 0.22212
44 Wakefield -71.0390 42.2885 22.2 0.07151
45 Quincy -71.0040 42.1425 19.8 0.24522
46 Belmont -71.1175 42.2350 29.6 0.06047
47 Boston Dorchester -71.0475 42.1690 14.9 7.75223
48 Framingham -71.2475 42.1825 26.2 0.19073
49 Sharon -71.1200 42.0725 22.5 0.06466
50 Needham -71.1405 42.1632 33.2 0.10469
51 Rockland -70.9470 42.0725 17.5 0.03113
52 Boston Roxbury -71.0520 42.1854 16.1 6.44405
53 Revere -71.0125 42.2462 21.8 0.17331
54 Boston Forest Hills -71.0565 42.1880 16.7 4.87141
55 Boston Allston-Brighton -71.0950 42.2120 21.7 3.84970
56 Peabody -71.0040 42.3350 26.6 0.12744
57 Hingham -70.9325 42.1230 31.2 0.03049
58 Chelsea -71.0189 42.2344 15.2 0.15086
59 Winthrop -70.9860 42.2312 22.4 0.06263
60 Boston Charlestown -71.0340 42.2260 13.9 15.28800
61 Newton -71.1208 42.2183 30.1 0.61470
62 Waltham -71.1500 42.2308 24.4 0.22969
63 Cambridge -71.0519 42.2230 15.6 4.09740
64 Boston East Boston -71.0195 42.2255 10.2 14.33370
65 Lynn -70.9640 42.2840 13.9 0.84054
66 Pembroke -70.8525 42.0300 20.6 0.10659
67 Waltham -71.1245 42.2277 25.0 0.19802
68 Peabody -70.9560 42.3125 16.6 0.22927
69 Cambridge -71.0620 42.2236 21.5 1.65660
70 Lynn -70.9720 42.2870 18.2 0.63796
71 Boston East Boston -71.0410 42.2290 10.9 15.87440
72 Chelsea -71.0228 42.2335 7.0 0.18337
73 Braintree -71.0100 42.1215 22.2 0.24103
74 Somerville -71.0750 42.2400 19.6 1.19294
75 Newton -71.1320 42.2142 29.0 0.44791
76 Wellesley -71.1480 42.1880 35.1 0.21038
77 Lynn -70.9775 42.2720 13.5 1.61282
78 Needham -71.1380 42.1733 32.0 0.09604
79 Burlington -71.1230 42.2920 23.4 0.19539
80 Wenham -70.9295 42.3715 31.6 0.01432
81 Cambridge -71.0810 42.2368 17.4 1.20742
82 Boston Roxbury -71.0455 42.1953 7.2 18.08460
83 Revere -71.0105 42.2547 23.1 0.17899
84 Lynn -70.9670 42.2790 12.7 1.13081
85 Boston Downtown -71.0455 42.2060 27.5 14.43830
86 Boston Mattapan -71.0833 42.1750 23.2 3.56868
87 Boston Roxbury -71.0618 42.1990 16.7 11.08740
88 Medford -71.0550 42.2505 19.8 0.12802
89 Sargus -71.0200 42.2875 24.2 0.17505
90 Malden -71.0285 42.2620 19.2 0.15098
91 Melrose -71.0420 42.2796 23.6 0.05660
92 Lynn -70.9780 42.2850 17.5 0.78420
93 Boston South Boston -71.0300 42.1988 12.1 9.59571
94 Boston Savin Hill -71.0460 42.1810 13.4 6.71772
95 Boston Roxbury -71.0650 42.2018 14.2 7.02259
96 Brookline -71.0835 42.2000 48.8 0.52014
97 Concord -71.2400 42.2725 30.3 0.04666
98 Sherborn -71.2230 42.1450 44.0 0.01538
99 Boston Savin Hill -71.0510 42.1780 14.1 4.75237
100 Melrose -71.0345 42.2785 28.7 0.05302
101 Natick -71.2138 42.1645 20.1 0.10612
102 Boston Mattapan -71.0660 42.1780 19.1 4.42228
103 Boston Allston-Brighton -71.1007 42.2100 22.7 5.20177
104 Boston Forest Hills -71.0528 42.1920 12.0 15.02340
105 Brookline -71.0765 42.2075 30.1 0.65665
106 Boston Downtown -71.0487 42.2048 17.2 14.05070
107 Marblehead -70.9280 42.2930 33.4 0.03237
108 Beverly -70.9215 42.3320 22.2 0.11027
109 Lynn -70.9880 42.2776 18.2 0.72580
110 Boston South Boston -71.0230 42.2090 5.0 38.35180
111 Boston Allston-Brighton -71.0930 42.2070 22.6 4.26131
112 Boston Back Bay -71.0595 42.2075 27.5 4.55587
113 Duxbury -70.8300 42.0485 30.1 0.01709
114 Newton -71.1285 42.1930 31.7 0.46296
115 Framingham -71.2550 42.1767 24.5 0.16439
116 Belmont -71.1000 42.2408 37.2 0.05780
117 Stoneham -71.0620 42.2960 25.0 0.02875
118 Cambridge -71.0555 42.2222 14.6 2.36862
119 Boston Dorchester -71.0430 42.1728 16.4 4.81213
120 Braintree -70.9834 42.1375 18.5 0.28392
121 Boston Roxbury -71.0505 42.1880 14.1 10.06230
122 Lynn -70.9597 42.2870 16.6 0.67191
123 Woburn -71.0910 42.3050 20.3 0.08387
124 Winthrop -70.9825 42.2210 19.0 0.04741
125 Waltham -71.1375 42.2355 28.1 0.14052
126 Quincy -71.0138 42.1555 22.8 0.49298
127 Malden -71.0285 42.2580 20.4 0.13058
128 Revere -70.9920 42.2380 16.8 0.22438
129 Boston Allston-Brighton -71.0905 42.2033 25.0 4.54192
130 Lynn -70.9693 42.2816 15.6 0.75026
131 Winchester -71.0800 42.2680 33.2 0.06860
132 Beverly -70.9385 42.3460 23.3 0.15445
133 Quincy -71.0150 42.1670 19.4 0.26169
134 Danvers -70.9510 42.3340 19.7 0.08873
135 Walpole -71.1325 42.0940 22.3 0.04590
136 Boston Downtown -71.0390 42.2198 11.9 20.71620
137 Boston Allston-Brighton -71.0865 42.2150 16.8 4.22239
138 Dedham -71.0940 42.1355 25.2 0.16211
139 Lynnfield -71.0130 42.3130 30.8 0.02763
140 Boston Back Bay -71.0590 42.2098 21.9 3.47428
141 Waltham -71.1435 42.2177 21.7 0.17446
142 Cambridge -71.0650 42.2223 23.3 1.42502
143 Wilmington -71.0900 42.3362 22.0 0.05789
144 Westwood -71.1385 42.1235 28.5 0.03502
145 Malden -71.0360 42.2608 21.2 0.13158
146 Winthrop -70.9910 42.2275 20.6 0.04527
147 Arlington -71.1125 42.2550 23.2 0.07022
148 Boston Savin Hill -71.0445 42.1917 8.2 15.17720
149 Cambridge -71.0510 42.2205 11.8 2.77974
150 Quincy -71.0201 42.1600 20.3 0.34940
151 Westwood -71.1330 42.1408 37.3 0.07886
152 Somerville -71.0580 42.2350 18.0 0.32543
153 Boston Savin Hill -71.0410 42.1795 15.2 5.44114
154 Somerville -71.0543 42.2265 14.4 1.62864
155 Everett -71.0370 42.2435 17.3 0.15038
156 Boston Roxbury -71.0525 42.1989 8.8 73.53410
157 Medford -71.0810 42.2450 26.5 0.11432
158 Framingham -71.2685 42.1935 29.6 0.08221
159 Holbrook -70.9960 42.0895 19.4 0.03466
160 Boston Forest Hills -71.0558 42.1913 14.6 10.23300
161 Quincy -70.9795 42.1580 16.2 0.25356
162 Norwood -71.1180 42.1217 21.7 0.08199
163 Peabody -70.9550 42.3165 14.4 0.25387
164 Boston South Boston -71.0300 42.2015 7.2 14.23620
165 Cambridge -71.0680 42.2150 15.6 3.53501
166 Boston Dorchester -71.0550 42.1685 19.0 3.77498
167 Newton -71.1491 42.2030 31.5 0.51183
168 Boston Savin Hill -71.0321 42.1925 10.5 22.05110
169 Norwood -71.1090 42.1100 23.9 0.08265
170 Beverly -70.9400 42.3320 19.6 0.10328
171 Waltham -71.1335 42.2250 22.4 0.21719
172 Newton -71.1215 42.2025 37.6 0.38214
173 Beverly -70.9075 42.3390 25.0 0.12650
174 Boston Charlestown -71.0400 42.2248 13.1 23.64820
175 Somerville -71.0745 42.2405 23.0 0.59005
176 Belmont -71.0966 42.2282 36.2 0.06888
177 Marshfield -70.8300 42.0775 24.1 0.07950
178 Lynn -70.9645 42.2920 20.4 0.62976
179 Wilmington -71.1080 42.3400 17.4 0.13554
180 Wilmington -71.1110 42.3270 20.9 0.12816
181 Boston Mattapan -71.0825 42.1698 19.6 4.03841
182 Stoneham -71.0615 42.2840 20.6 0.04294
183 Cambridge -71.0567 42.2240 17.8 2.33099
184 Sudbury -71.2630 42.2225 32.9 0.01778
185 Boston Roxbury -71.0665 42.1970 20.8 12.04820
186 Salem -70.9375 42.3100 22.1 0.14455
187 Newton -71.1160 42.1947 31.6 0.41238
188 Norwood -71.1280 42.1108 27.1 0.05372
189 Watertown -71.1060 42.2185 23.0 0.11425
190 Weymouth -70.9675 42.1440 19.0 0.05497
191 Boston South Boston -71.0345 42.2001 8.5 41.52920
192 Lynn -70.9820 42.2810 19.6 0.85204
193 Cambridge -71.0670 42.2245 24.3 1.34284
194 Boston Savin Hill -71.0370 42.1940 12.8 9.39063
195 Canton -71.0875 42.0950 22.0 0.03537
196 Cambridge -71.0622 42.2205 19.6 1.49632
197 Burlington -71.1210 42.3160 24.2 0.08826
198 Boston East Boston -71.0245 42.2235 8.8 20.08490
199 Concord -71.2200 42.2715 33.3 0.04011
200 Somerville -71.0790 42.2450 18.4 0.32982
201 Boston Roxbury -71.0577 42.1967 8.3 15.86030
202 Boston South Boston -71.0330 42.2032 6.3 9.91655
203 Newton -71.1400 42.1946 41.7 0.57529
204 Boston South Boston -71.0200 42.1993 12.7 13.35980
205 Belmont -71.1120 42.2300 26.4 0.08308
206 Danvers -70.9580 42.3460 25.0 0.05360
207 Winchester -71.0760 42.2775 28.4 0.12204
208 Medford -71.0662 42.2425 19.5 0.13262
209 Hingham -70.9370 42.1475 23.9 0.02543
210 Newton -71.1300 42.1880 24.3 0.53700
211 Newton -71.1210 42.2166 21.7 0.40771
212 Lynn -70.9640 42.2765 14.5 1.35472
213 Chelsea -71.0160 42.2382 13.6 0.10574
214 Boston Roxbury -71.0547 42.1890 14.5 8.49213
215 Salem -70.9440 42.3170 18.9 0.11747
216 Scituate -70.8330 42.1150 22.9 0.06211
217 Canton -71.0625 42.1020 26.4 0.09266
218 Braintree -70.9995 42.1175 23.0 0.30347
219 Cambridge -71.0590 42.2170 15.3 1.12658
220 Somerville -71.0620 42.2275 13.3 0.24980
221 Boston Roxbury -71.0485 42.1920 9.5 9.33889
222 Needham -71.1395 42.1813 29.1 0.07978
223 Boston Dorchester -71.0450 42.1640 21.4 7.83932
224 Boston Dorchester -71.0550 42.1650 19.9 3.16360
225 Winchester -71.0890 42.2715 38.7 0.12083
226 Manchester -70.8600 42.3450 33.0 0.01951
227 Boston West Roxbury -71.0950 42.1730 23.7 5.70818
228 Boston West Roxbury -71.0975 42.1608 21.8 2.81838
229 Brookline -71.0925 42.2075 43.5 0.54050
230 Boston Downtown -71.0462 42.2075 15.0 51.13580
231 Boston West Roxbury -71.1008 42.1740 23.0 5.82401
232 Cambridge -71.0590 42.2210 15.6 2.15505
233 Boston Savin Hill -71.0425 42.1890 11.8 10.67180
234 Weston -71.1990 42.2320 48.5 0.03510
235 Cambridge -71.0775 42.2351 22.3 2.44953
236 Randolph -71.0275 42.1130 19.3 0.06617
237 Beverly -70.9300 42.3275 16.0 0.17171
238 Peabody -70.9650 42.3075 20.0 0.18836
239 Boston Dorchester -71.0515 42.1700 13.5 8.20058
240 Arlington -71.0855 42.2450 23.6 0.09178
241 Framingham -71.2575 42.1618 18.5 0.19133
242 Boston Hyde Park -71.0650 42.1610 20.6 4.83567
243 Dedham -71.0980 42.1540 20.7 0.09065
244 Belmont -71.0995 42.2345 39.8 0.06588
245 Sargus -71.0000 42.2700 18.9 0.06417
246 Newton -71.1100 42.2137 26.7 0.35809
247 Malden -71.0396 42.2512 18.5 0.14231
248 Weymouth -70.9602 42.1270 18.5 0.03041
249 Peabody -71.0030 42.3235 25.3 0.14150
250 Lynn -70.9597 42.2825 14.8 0.95577
251 Boston Charlestown -71.0400 42.2275 10.2 17.86670
252 Lexington -71.1450 42.2651 34.9 0.08370
253 Boston Dorchester -71.0430 42.1690 20.0 6.80117
254 Danvers -70.9630 42.3382 20.5 0.04337
255 Boston Savin Hill -71.0475 42.1842 11.7 13.91340
256 Lynn -70.9510 42.2780 21.0 1.00245
257 Arlington -71.0965 42.2470 29.9 0.06642
258 Boston South Boston -71.0260 42.1993 12.5 5.87205
259 Brookline -71.0800 42.2040 33.8 0.54011
260 Brookline -71.0750 42.1980 31.0 0.82526
261 Framingham -71.2380 42.1910 24.4 0.14030
262 Boston Charlestown -71.0367 42.2240 15.0 19.60910
263 Ashland -71.2895 42.1575 21.9 0.04819
264 Burlington -71.1300 42.3050 22.8 0.09164
265 Malden -71.0355 42.2545 18.3 0.17134
266 Boston Roxbury -71.0420 42.1985 7.0 45.74610
267 Beverly -70.9300 42.3370 18.7 0.14932
268 Boston East Boston -71.0230 42.2287 11.3 9.18702
269 Boston Hyde Park -71.0750 42.1455 21.2 3.67367
270 Salem -70.9290 42.3160 18.9 0.17004
271 Boston Savin Hill -71.0455 42.1893 9.6 14.42080
272 Randolph -71.0415 42.1150 22.6 0.06724
273 Woburn -71.0900 42.2835 20.0 0.10153
274 Boston East Boston -71.0228 42.2231 10.5 24.39380
275 Scituate -70.8550 42.1300 26.6 0.02899
276 Boston Hyde Park -71.0715 42.1550 19.1 5.69175
277 Boston Downtown -71.0451 42.2015 16.3 28.65580
278 Sharon -71.1000 42.0590 29.0 0.05561
279 Lexington -71.1340 42.2785 36.4 0.08664
280 Revere -70.9947 42.2496 18.3 0.26838
281 Boston Savin Hill -71.0390 42.1868 14.9 6.28807
282 Boston Roxbury -71.0477 42.1965 7.5 10.83420
283 Boston Savin Hill -71.0400 42.1820 14.1 9.32909
284 Natick -71.2320 42.1725 23.7 0.12757
285 Wakefield -71.0500 42.3000 26.6 0.05735
286 Westwood -71.1070 42.1307 27.9 0.03615
287 Boston Forest Hills -71.0550 42.1830 13.8 8.05579
288 Reading -71.0675 42.3260 24.8 0.03659
289 Boston Mattapan -71.0690 42.1725 20.1 13.07510
290 Cambridge -71.0866 42.2345 23.8 1.80028
291 Canton -71.0865 42.1200 33.1 0.10000
292 Boston East Boston -71.0109 42.2300 15.1 6.96215
293 Woburn -71.0905 42.2950 21.2 0.05646
294 Everett -71.0262 42.2431 21.4 0.16902
295 Revere -71.0050 42.2455 21.2 0.23912
296 Middleton -71.0175 42.3715 18.9 0.01360
297 Peabody -70.9725 42.3075 19.3 0.17142
298 Boston Savin Hill -71.0340 42.1890 14.8 5.66637
299 Medford -71.0622 42.2431 19.5 0.17120
300 Sudbury -71.2440 42.2425 34.9 0.03150
301 Everett -71.0377 42.2470 20.5 0.09299
302 Revere -71.0125 42.2500 24.5 0.27957
303 Bedford -71.1525 42.3000 31.1 0.02187
304 Medford -71.0690 42.2480 20.1 0.13960
305 Boston Roxbury -71.0590 42.1920 11.7 8.79212
306 Boston North End -71.0339 42.2207 13.8 11.10810
307 Malden -71.0490 42.2530 18.8 0.12329
308 Boston Dorchester -71.0358 42.1675 20.2 5.82115
309 Walpole -71.1550 42.0980 24.8 0.04297
310 Somerville -71.0530 42.2355 16.2 0.25915
311 Cambridge -71.0792 42.2390 19.1 2.31390
312 North Reading -71.0690 42.3525 19.4 0.04379
313 Arlington -71.1060 42.2512 24.6 0.05425
314 Quincy -71.0100 42.1500 23.1 0.40202
315 Quincy -71.0000 42.1530 23.8 0.36920
316 Peabody -70.9775 42.3237 24.7 0.15936
317 Weymouth -70.9760 42.1280 19.5 0.03427
318 Chelsea -71.0245 42.2368 8.1 0.20746
319 Quincy -71.0075 42.1613 21.6 0.26938
320 Chelsea -71.0297 42.2447 20.1 0.11132
321 Arlington -71.0833 42.2475 22.6 0.08447
322 Lynn -70.9925 42.2825 20.2 0.80271
323 Norwood -71.1210 42.1025 20.3 0.14103
324 Hamilton -70.9300 42.3740 24.7 0.02055
325 Walpole -71.1480 42.0775 23.2 0.03871
326 Boston Roxbury -71.0558 42.1937 10.2 12.24720
327 Boston Roxbury -71.0586 42.1999 8.4 11.81230
328 Needham -71.1495 42.1730 33.1 0.06127
329 Bedford -71.1633 42.3030 29.1 0.01439
330 Boston Charlestown -71.0380 42.2265 13.3 9.82349
331 Hull -70.9150 42.1600 16.5 0.02498
332 Milton -71.0280 42.1565 28.2 0.04932
333 Boston South Boston -71.0250 42.2024 8.5 7.67202
334 Malden -71.0190 42.2605 19.3 0.14476
335 Boston South Boston -71.0220 42.1996 13.1 8.71675
336 Somerville -71.0692 42.2390 19.2 0.34006
337 Framingham -71.2435 42.1630 17.6 0.20608
338 Boston Roxbury -71.0542 42.1961 10.9 37.66190
339 Marblehead -70.9220 42.2980 36.2 0.06905
340 Everett -71.0308 42.2450 18.8 0.09849
341 Newton -71.1485 42.2110 25.1 0.52058
342 Brookline -71.0710 42.2030 36.5 0.55007
343 Wakefield -71.0430 42.3075 23.9 0.05059
344 Cambridge -71.0918 42.2265 22.7 2.24236
345 Watertown -71.1166 42.2230 23.3 0.04560
346 Boston Savin Hill -71.0370 42.1840 12.6 9.92485
347 Quincy -71.0075 42.1780 22.1 0.79041
348 Boston South Boston -71.0326 42.2011 5.6 25.04610
349 Topsfield -70.9625 42.3810 35.4 0.01311
350 Boston Roxbury -71.0619 42.1948 13.4 7.05042
351 Lexington -71.1408 42.2550 29.8 0.12579
352 Marblehead -70.9165 42.3040 28.7 0.02985
353 Medford -71.0770 42.2490 19.3 0.21161
354 Boston East Boston -71.0035 42.2330 23.2 5.29305
355 Cambridge -71.0580 42.2230 15.4 2.73397
356 Newton -71.1380 42.2141 24.0 0.33045
357 Lynn -70.9795 42.2760 13.1 1.15172
358 Salem -70.9360 42.2970 22.9 0.08829
359 Everett -71.0333 42.2395 15.7 0.38735
360 Peabody -70.9675 42.3170 21.2 0.12269
361 Brookline -71.0725 42.1965 30.7 0.78570
362 Waltham -71.1490 42.2268 22.5 0.25199
363 Belmont -71.1005 42.2287 37.9 0.09103
364 Millis -71.2135 42.1000 22.0 0.01096
365 Brookline -71.0727 42.2077 36.0 0.66351
366 Winchester -71.0982 42.2685 43.8 0.08187
367 Wayland -71.2185 42.1955 24.1 0.03445
368 Lynn -70.9870 42.2985 23.1 1.05393
369 North Reading -71.0450 42.3450 23.5 0.03584
370 Sargus -71.0080 42.2745 20.0 0.09744
371 Boston Dorchester -71.0350 42.1745 17.7 3.69311
372 Medfield -71.1800 42.1105 32.2 0.00906
373 Salem -70.9350 42.3160 15.0 0.22489
374 Lynnfield -71.0300 42.3240 34.9 0.03359
375 Wellesley -71.1775 42.1735 46.0 0.06129
376 Boston Allston-Brighton -71.0810 42.2080 20.8 3.67822
377 Newton -71.1100 42.2060 44.8 0.31533
378 Brookline -71.0700 42.2000 22.8 0.76162
379 Boston Allston-Brighton -71.0830 42.2172 17.8 8.98296
380 Framingham -71.2470 42.1703 20.5 0.19657
381 Everett -71.0243 42.2483 22.0 0.06899
382 Boston Savin Hill -71.0405 42.1920 10.8 12.80230
383 Framingham -71.2807 42.1727 42.8 0.36894
384 Boston South Boston -71.0320 42.2057 5.0 67.92080
385 Arlington -71.0940 42.2575 29.4 0.06664
386 Quincy -70.9880 42.1485 17.8 0.31827
387 Natick -71.2035 42.1870 23.3 0.09252
388 Framingham -71.2435 42.1982 24.8 0.21409
389 Revere -71.0010 42.2525 19.7 0.28960
390 Framingham -71.2620 42.1690 24.3 0.33983
391 Lynn -70.9765 42.2940 19.9 0.62739
392 Lynn -70.9835 42.2770 13.6 1.25179
393 Holbrook -71.0115 42.0865 17.1 0.05023
394 Boston Dorchester -71.0310 42.1695 19.5 6.65492
395 Boston East Boston -71.0168 42.2281 11.5 8.15174
396 Lexington -71.1220 42.2565 32.0 0.07875
397 Waltham -71.1475 42.2445 22.6 0.13642
398 Melrose -71.0300 42.2720 22.6 0.04684
399 Malden -71.0475 42.2610 22.8 0.10084
400 Cambridge -71.0820 42.2295 25.0 2.92400
401 Concord -71.2220 42.2890 34.6 0.03768
402 Boston Savin Hill -71.0378 42.1892 15.4 9.96654
403 Cambridge -71.0700 42.2214 27.0 1.27346
404 Arlington -71.0870 42.2416 23.1 0.13914
405 Cambridge -71.0667 42.2200 13.1 2.44668
406 Braintree -70.9895 42.1265 25.0 0.34109
407 Braintree -70.9975 42.1340 23.1 0.18159
408 Swampscott -70.9360 42.2830 34.7 0.02729
409 Boston Mattapan -71.0700 42.1680 19.1 15.57570
410 Swampscott -70.9500 42.2875 21.6 0.02731
411 Boston Roxbury -71.0521 42.1940 11.0 7.36711
412 Waltham -71.1270 42.2350 23.7 0.28955
413 Dedham -71.1170 42.1510 35.2 0.22188
414 Milton -71.0570 42.1525 28.4 0.05479
415 Lynn -70.9775 42.2790 15.2 1.23247
416 Brookline -71.0790 42.2020 43.1 0.53412
417 Boston Back Bay -71.0540 42.2052 21.9 3.69695
418 Wakefield -71.0330 42.3050 22.5 0.05188
419 Boston Hyde Park -71.0804 42.1540 20.6 2.37857
420 Boston Downtown -71.0470 42.2030 17.9 18.81100
421 Medford -71.0553 42.2465 20.4 0.13117
422 Boston North End -71.0346 42.2187 13.8 18.49820
423 Waltham -71.1380 42.2216 19.3 0.37578
424 Revere -70.9985 42.2430 17.5 0.17783
425 Woburn -71.1105 42.2890 24.1 0.07896
426 Rockland -70.9501 42.0825 17.2 0.06162
427 Somerville -71.0638 42.2368 14.3 0.88125
428 Boston Back Bay -71.0508 42.2070 23.1 13.52220
429 Somerville -71.0518 42.2312 14.0 0.29090
430 Watertown -71.0933 42.2220 21.5 0.11069
431 Boston Roxbury -71.0498 42.2007 10.4 25.94060
432 Waltham -71.1430 42.2217 20.0 0.43571
433 Lynn -70.9570 42.2800 18.4 0.77299
434 Sharon -71.1035 42.0735 24.8 0.04417
435 Boston West Roxbury -71.0900 42.1665 25.0 5.73116
436 Needham -71.1305 42.1675 32.4 0.05644
437 Lexington -71.1216 42.2700 33.0 0.09068
438 Newton -71.1190 42.1807 31.5 0.44178
439 Cambridge -71.0540 42.2190 13.8 2.37934
440 Milton -71.0450 42.1590 33.4 0.07503
441 Cohasset -70.8875 42.1465 32.7 0.01301
442 Somerville -71.0568 42.2300 17.8 0.54452
443 Somerville -71.0750 42.2362 18.1 0.55778
444 Woburn -71.0760 42.2895 20.8 0.08707
445 Winchester -71.0850 42.2775 21.4 0.11504
446 Newton -71.1340 42.2040 48.3 0.33147
447 Boston Dorchester -71.0517 42.1735 12.7 4.66883
448 Quincy -71.0160 42.1760 16.1 2.63548
449 Braintree -71.0085 42.1290 23.8 0.16760
450 Medford -71.0560 42.2605 21.7 0.10793
451 Nahant -70.9550 42.2550 24.0 0.00632
452 Boston Savin Hill -71.0340 42.1790 16.1 5.09017
453 Weymouth -70.9633 42.1374 20.6 0.03306
454 Medford -71.0812 42.2515 18.6 0.22876
455 Weymouth -70.9700 42.1150 21.1 0.03961
456 Wellesley -71.1850 42.1848 35.4 0.03705
457 Somerville -71.0630 42.2325 17.1 0.35233
458 Braintree -70.9900 42.1367 20.4 0.35114
459 Woburn -71.1000 42.2875 21.4 0.09512
460 Boston East Boston -71.0215 42.2248 7.4 22.59710
461 Sargus -71.0040 42.2865 21.0 0.08014
462 Reading -71.0575 42.3170 22.9 0.03551
463 Reading -71.0690 42.3150 23.9 0.04462
464 Natick -71.2200 42.1782 22.2 0.10290
465 Dedham -71.0870 42.1410 21.1 0.29916
466 Boston Downtown -71.0437 42.2050 17.2 7.40389
467 Boston East Boston -71.0235 42.2213 7.2 16.81180
468 Braintree -71.0000 42.1066 24.6 0.19186
469 Everett -71.0312 42.2505 20.3 0.07165
470 Newton -71.1100 42.1850 46.7 0.29819
471 Ashland -71.2690 42.1482 20.9 0.03548
472 Boston Savin Hill -71.0460 42.1867 13.4 11.16040
473 Hanover -70.9275 42.0795 23.1 0.01870
474 Newton -71.1250 42.2134 27.5 0.62356
475 Reading -71.0685 42.3080 28.0 0.04113
476 Cambridge -71.0740 42.2331 23.8 2.30040
477 Belmont -71.1065 42.2300 32.5 0.10008
478 Winthrop -70.9948 42.2260 23.9 0.06076
479 Waltham -71.1503 42.2220 24.4 0.13587
480 Danvers -70.9780 42.3425 23.4 0.04981
zoned_25k_p indust_p borders_charles NOx n_rooms_avg before_1940_p
1 0.0 21.89 0 0.6240 5.942 93.5
2 0.0 5.19 0 0.5150 5.968 58.5
3 0.0 18.10 0 0.7000 5.520 100.0
4 0.0 18.10 0 0.5840 6.162 97.4
5 0.0 18.10 0 0.7130 6.728 94.1
6 0.0 10.81 0 0.4130 5.961 17.5
7 0.0 18.10 0 0.7130 7.393 99.3
8 0.0 18.10 0 0.7400 5.935 87.9
9 0.0 13.89 0 0.5500 6.642 85.1
10 80.0 1.91 0 0.4130 5.663 21.9
11 20.0 3.33 0 0.4429 7.820 64.5
12 0.0 19.58 1 0.8710 5.403 100.0
13 0.0 18.10 0 0.7400 6.406 97.2
14 0.0 18.10 0 0.5800 6.167 84.0
15 0.0 11.93 0 0.5730 6.794 89.3
16 0.0 18.10 0 0.7130 6.436 87.9
17 0.0 8.14 0 0.5380 5.813 100.0
18 12.5 7.87 0 0.5240 5.889 39.0
19 0.0 18.10 0 0.7700 6.251 91.1
20 0.0 18.10 0 0.6590 5.608 100.0
21 0.0 8.56 0 0.5200 6.229 91.2
22 20.0 6.96 0 0.4640 6.538 58.7
23 30.0 4.93 0 0.4280 6.481 18.5
24 0.0 19.58 1 0.8710 6.129 96.0
25 60.0 1.69 0 0.4110 5.884 18.5
26 80.0 2.01 0 0.4350 6.635 29.7
27 0.0 13.92 0 0.4370 6.678 31.1
28 0.0 18.10 0 0.6140 6.229 88.0
29 0.0 18.10 0 0.7000 5.036 97.0
30 0.0 3.41 0 0.4890 6.405 73.9
31 0.0 6.91 0 0.4480 5.602 62.0
32 0.0 18.10 0 0.6710 6.968 91.9
33 0.0 19.58 0 0.8710 5.709 98.5
34 0.0 8.56 0 0.5200 6.727 79.9
35 0.0 18.10 0 0.6930 6.193 92.6
36 0.0 8.14 0 0.5380 5.950 82.0
37 0.0 21.89 0 0.6240 5.757 98.4
38 0.0 18.10 0 0.6140 6.980 67.6
39 0.0 18.10 0 0.7130 6.417 98.3
40 0.0 19.58 0 0.6050 6.943 97.4
41 0.0 5.19 0 0.5150 6.310 38.5
42 28.0 15.04 0 0.4640 6.442 53.6
43 0.0 10.01 0 0.5470 6.092 95.4
44 0.0 4.49 0 0.4490 6.121 56.8
45 0.0 9.90 0 0.5440 5.782 71.7
46 0.0 2.46 0 0.4880 6.153 68.8
47 0.0 18.10 0 0.7130 6.301 83.7
48 22.0 5.86 0 0.4310 6.718 17.5
49 70.0 2.24 0 0.4000 6.345 20.1
50 40.0 6.41 1 0.4470 7.267 49.0
51 0.0 4.39 0 0.4420 6.014 48.5
52 0.0 18.10 0 0.5840 6.425 74.8
53 0.0 9.69 0 0.5850 5.707 54.0
54 0.0 18.10 0 0.6140 6.484 93.6
55 0.0 18.10 1 0.7700 6.395 91.0
56 0.0 6.91 0 0.4480 6.770 2.9
57 55.0 3.78 0 0.4840 6.874 28.1
58 0.0 27.74 0 0.6090 5.454 92.7
59 0.0 11.93 0 0.5730 6.593 69.1
60 0.0 18.10 0 0.6710 6.649 93.3
61 0.0 6.20 0 0.5070 6.618 80.8
62 0.0 10.59 0 0.4890 6.326 52.5
63 0.0 19.58 0 0.8710 5.468 100.0
64 0.0 18.10 0 0.7000 4.880 100.0
65 0.0 8.14 0 0.5380 5.599 85.7
66 80.0 1.91 0 0.4130 5.936 19.5
67 0.0 10.59 0 0.4890 6.182 42.4
68 0.0 6.91 0 0.4480 6.030 85.5
69 0.0 19.58 0 0.8710 6.122 97.3
70 0.0 8.14 0 0.5380 6.096 84.5
71 0.0 18.10 0 0.6710 6.545 99.1
72 0.0 27.74 0 0.6090 5.414 98.3
73 0.0 7.38 0 0.4930 6.083 43.7
74 0.0 21.89 0 0.6240 6.326 97.7
75 0.0 6.20 1 0.5070 6.726 66.5
76 20.0 3.33 0 0.4429 6.812 32.2
77 0.0 8.14 0 0.5380 6.096 96.9
78 40.0 6.41 0 0.4470 6.854 42.8
79 0.0 10.81 0 0.4130 6.245 6.2
80 100.0 1.32 0 0.4110 6.816 40.5
81 0.0 19.58 0 0.6050 5.875 94.6
82 0.0 18.10 0 0.6790 6.434 100.0
83 0.0 9.69 0 0.5850 5.670 28.8
84 0.0 8.14 0 0.5380 5.713 94.1
85 0.0 18.10 0 0.5970 6.852 100.0
86 0.0 18.10 0 0.5800 6.437 75.0
87 0.0 18.10 0 0.7180 6.411 100.0
88 0.0 8.56 0 0.5200 6.474 97.1
89 0.0 5.96 0 0.4990 5.966 30.2
90 0.0 10.01 0 0.5470 6.021 82.6
91 0.0 3.41 0 0.4890 7.007 86.3
92 0.0 8.14 0 0.5380 5.990 81.7
93 0.0 18.10 0 0.6930 6.404 100.0
94 0.0 18.10 0 0.7130 6.749 92.6
95 0.0 18.10 0 0.7180 6.006 95.3
96 20.0 3.97 0 0.6470 8.398 91.5
97 80.0 1.52 0 0.4040 7.107 36.6
98 90.0 3.75 0 0.3940 7.454 34.2
99 0.0 18.10 0 0.7130 6.525 86.5
100 0.0 3.41 0 0.4890 7.079 63.1
101 30.0 4.93 0 0.4280 6.095 65.1
102 0.0 18.10 0 0.5840 6.003 94.5
103 0.0 18.10 1 0.7700 6.127 83.4
104 0.0 18.10 0 0.6140 5.304 97.3
105 20.0 3.97 0 0.6470 6.842 100.0
106 0.0 18.10 0 0.5970 6.657 100.0
107 0.0 2.18 0 0.4580 6.998 45.8
108 25.0 5.13 0 0.4530 6.456 67.8
109 0.0 8.14 0 0.5380 5.727 69.5
110 0.0 18.10 0 0.6930 5.453 100.0
111 0.0 18.10 0 0.7700 6.112 81.3
112 0.0 18.10 0 0.7180 3.561 87.9
113 90.0 2.02 0 0.4100 6.728 36.1
114 0.0 6.20 0 0.5040 7.412 76.9
115 22.0 5.86 0 0.4310 6.433 49.1
116 0.0 2.46 0 0.4880 6.980 58.4
117 28.0 15.04 0 0.4640 6.211 28.9
118 0.0 19.58 0 0.8710 4.926 95.7
119 0.0 18.10 0 0.7130 6.701 90.0
120 0.0 7.38 0 0.4930 5.708 74.3
121 0.0 18.10 0 0.5840 6.833 94.3
122 0.0 8.14 0 0.5380 5.813 90.3
123 0.0 12.83 0 0.4370 5.874 36.6
124 0.0 11.93 0 0.5730 6.030 80.8
125 0.0 10.59 0 0.4890 6.375 32.3
126 0.0 9.90 0 0.5440 6.635 82.5
127 0.0 10.01 0 0.5470 5.872 73.1
128 0.0 9.69 0 0.5850 6.027 79.7
129 0.0 18.10 0 0.7700 6.398 88.0
130 0.0 8.14 0 0.5380 5.924 94.1
131 0.0 2.89 0 0.4450 7.416 62.5
132 25.0 5.13 0 0.4530 6.145 29.2
133 0.0 9.90 0 0.5440 6.023 90.4
134 21.0 5.64 0 0.4390 5.963 45.7
135 52.5 5.32 0 0.4050 6.315 45.6
136 0.0 18.10 0 0.6590 4.138 100.0
137 0.0 18.10 1 0.7700 5.803 89.0
138 20.0 6.96 0 0.4640 6.240 16.3
139 75.0 2.95 0 0.4280 6.595 21.8
140 0.0 18.10 1 0.7180 8.780 82.9
141 0.0 10.59 1 0.4890 5.960 92.1
142 0.0 19.58 0 0.8710 6.510 100.0
143 12.5 6.07 0 0.4090 5.878 21.4
144 80.0 4.95 0 0.4110 6.861 27.9
145 0.0 10.01 0 0.5470 6.176 72.5
146 0.0 11.93 0 0.5730 6.120 76.7
147 0.0 4.05 0 0.5100 6.020 47.2
148 0.0 18.10 0 0.7400 6.152 100.0
149 0.0 19.58 0 0.8710 4.903 97.8
150 0.0 9.90 0 0.5440 5.972 76.7
151 80.0 4.95 0 0.4110 7.148 27.7
152 0.0 21.89 0 0.6240 6.431 98.8
153 0.0 18.10 0 0.7130 6.655 98.2
154 0.0 21.89 0 0.6240 5.019 100.0
155 0.0 25.65 0 0.5810 5.856 97.0
156 0.0 18.10 0 0.6790 5.957 100.0
157 0.0 8.56 0 0.5200 6.781 71.3
158 22.0 5.86 0 0.4310 6.957 6.8
159 35.0 6.06 0 0.4379 6.031 23.3
160 0.0 18.10 0 0.6140 6.185 96.7
161 0.0 9.90 0 0.5440 5.705 77.7
162 0.0 13.92 0 0.4370 6.009 42.3
163 0.0 6.91 0 0.4480 5.399 95.3
164 0.0 18.10 0 0.6930 6.343 100.0
165 0.0 19.58 1 0.8710 6.152 82.6
166 0.0 18.10 0 0.6550 5.952 84.7
167 0.0 6.20 0 0.5070 7.358 71.6
168 0.0 18.10 0 0.7400 5.818 92.4
169 0.0 13.92 0 0.4370 6.127 18.4
170 25.0 5.13 0 0.4530 5.927 47.2
171 0.0 10.59 1 0.4890 5.807 53.8
172 0.0 6.20 0 0.5040 8.040 86.5
173 25.0 5.13 0 0.4530 6.762 43.4
174 0.0 18.10 0 0.6710 6.380 96.2
175 0.0 21.89 0 0.6240 6.372 97.9
176 0.0 2.46 0 0.4880 6.144 62.2
177 60.0 1.69 0 0.4110 6.579 35.9
178 0.0 8.14 0 0.5380 5.949 61.8
179 12.5 6.07 0 0.4090 5.594 36.8
180 12.5 6.07 0 0.4090 5.885 33.0
181 0.0 18.10 0 0.5320 6.229 90.7
182 28.0 15.04 0 0.4640 6.249 77.3
183 0.0 19.58 0 0.8710 5.186 93.8
184 95.0 1.47 0 0.4030 7.135 13.9
185 0.0 18.10 0 0.6140 5.648 87.6
186 12.5 7.87 0 0.5240 6.172 96.1
187 0.0 6.20 0 0.5040 7.163 79.9
188 0.0 13.92 0 0.4370 6.549 51.0
189 0.0 13.89 1 0.5500 6.373 92.4
190 0.0 5.19 0 0.5150 5.985 45.4
191 0.0 18.10 0 0.6930 5.531 85.4
192 0.0 8.14 0 0.5380 5.965 89.2
193 0.0 19.58 0 0.6050 6.066 100.0
194 0.0 18.10 0 0.7400 5.627 93.9
195 34.0 6.09 0 0.4330 6.590 40.4
196 0.0 19.58 0 0.8710 5.404 100.0
197 0.0 10.81 0 0.4130 6.417 6.6
198 0.0 18.10 0 0.7000 4.368 91.2
199 80.0 1.52 0 0.4040 7.287 34.1
200 0.0 21.89 0 0.6240 5.822 95.4
201 0.0 18.10 0 0.6790 5.896 95.4
202 0.0 18.10 0 0.6930 5.852 77.8
203 0.0 6.20 0 0.5070 8.337 73.3
204 0.0 18.10 0 0.6930 5.887 94.7
205 0.0 2.46 0 0.4880 5.604 89.8
206 21.0 5.64 0 0.4390 6.511 21.1
207 0.0 2.89 0 0.4450 6.625 57.8
208 0.0 8.56 0 0.5200 5.851 96.7
209 55.0 3.78 0 0.4840 6.696 56.4
210 0.0 6.20 0 0.5040 5.981 68.1
211 0.0 6.20 1 0.5070 6.164 91.3
212 0.0 8.14 0 0.5380 6.072 100.0
213 0.0 27.74 0 0.6090 5.983 98.8
214 0.0 18.10 0 0.5840 6.348 86.1
215 12.5 7.87 0 0.5240 6.009 82.9
216 40.0 1.25 0 0.4290 6.490 44.4
217 34.0 6.09 0 0.4330 6.495 18.4
218 0.0 7.38 0 0.4930 6.312 28.9
219 0.0 19.58 1 0.8710 5.012 88.0
220 0.0 21.89 0 0.6240 5.857 98.2
221 0.0 18.10 0 0.6790 6.380 95.6
222 40.0 6.41 0 0.4470 6.482 32.1
223 0.0 18.10 0 0.6550 6.209 65.4
224 0.0 18.10 0 0.6550 5.759 48.2
225 0.0 2.89 0 0.4450 8.069 76.0
226 17.5 1.38 0 0.4161 7.104 59.5
227 0.0 18.10 0 0.5320 6.750 74.9
228 0.0 18.10 0 0.5320 5.762 40.3
229 20.0 3.97 0 0.5750 7.470 52.6
230 0.0 18.10 0 0.5970 5.757 100.0
231 0.0 18.10 0 0.5320 6.242 64.7
232 0.0 19.58 0 0.8710 5.628 100.0
233 0.0 18.10 0 0.7400 6.459 94.8
234 95.0 2.68 0 0.4161 7.853 33.2
235 0.0 19.58 0 0.6050 6.402 95.2
236 0.0 3.24 0 0.4600 5.868 25.8
237 25.0 5.13 0 0.4530 5.966 93.4
238 0.0 6.91 0 0.4480 5.786 33.3
239 0.0 18.10 0 0.7130 5.936 80.3
240 0.0 4.05 0 0.5100 6.416 84.1
241 22.0 5.86 0 0.4310 5.605 70.2
242 0.0 18.10 0 0.5830 5.905 53.2
243 20.0 6.96 1 0.4640 5.920 61.5
244 0.0 2.46 0 0.4880 7.765 83.3
245 0.0 5.96 0 0.4990 5.933 68.2
246 0.0 6.20 1 0.5070 6.951 88.5
247 0.0 10.01 0 0.5470 6.254 84.2
248 0.0 5.19 0 0.5150 5.895 59.6
249 0.0 6.91 0 0.4480 6.169 6.6
250 0.0 8.14 0 0.5380 6.047 88.8
251 0.0 18.10 0 0.6710 6.223 100.0
252 45.0 3.44 0 0.4370 7.185 38.9
253 0.0 18.10 0 0.7130 6.081 84.4
254 21.0 5.64 0 0.4390 6.115 63.0
255 0.0 18.10 0 0.7130 6.208 95.0
256 0.0 8.14 0 0.5380 6.674 87.3
257 0.0 4.05 0 0.5100 6.860 74.4
258 0.0 18.10 0 0.6930 6.405 96.0
259 20.0 3.97 0 0.6470 7.203 81.8
260 20.0 3.97 0 0.6470 7.327 94.5
261 22.0 5.86 0 0.4310 6.487 13.0
262 0.0 18.10 0 0.6710 7.313 97.9
263 80.0 3.64 0 0.3920 6.108 32.0
264 0.0 10.81 0 0.4130 6.065 7.8
265 0.0 10.01 0 0.5470 5.928 88.2
266 0.0 18.10 0 0.6930 4.519 100.0
267 25.0 5.13 0 0.4530 5.741 66.2
268 0.0 18.10 0 0.7000 5.536 100.0
269 0.0 18.10 0 0.5830 6.312 51.9
270 12.5 7.87 0 0.5240 6.004 85.9
271 0.0 18.10 0 0.7400 6.461 93.3
272 0.0 3.24 0 0.4600 6.333 17.2
273 0.0 12.83 0 0.4370 6.279 74.5
274 0.0 18.10 0 0.7000 4.652 100.0
275 40.0 1.25 0 0.4290 6.939 34.5
276 0.0 18.10 0 0.5830 6.114 79.8
277 0.0 18.10 0 0.5970 5.155 100.0
278 70.0 2.24 0 0.4000 7.041 10.0
279 45.0 3.44 0 0.4370 7.178 26.3
280 0.0 9.69 0 0.5850 5.794 70.6
281 0.0 18.10 0 0.7400 6.341 96.4
282 0.0 18.10 0 0.6790 6.782 90.8
283 0.0 18.10 0 0.7130 6.185 98.7
284 30.0 4.93 0 0.4280 6.393 7.8
285 0.0 4.49 0 0.4490 6.630 56.1
286 80.0 4.95 0 0.4110 6.630 23.4
287 0.0 18.10 0 0.5840 5.427 95.4
288 25.0 4.86 0 0.4260 6.302 32.2
289 0.0 18.10 0 0.5800 5.713 56.7
290 0.0 19.58 0 0.6050 5.877 79.2
291 34.0 6.09 0 0.4330 6.982 17.7
292 0.0 18.10 0 0.7000 5.713 97.0
293 0.0 12.83 0 0.4370 6.232 53.7
294 0.0 25.65 0 0.5810 5.986 88.4
295 0.0 9.69 0 0.5850 6.019 65.3
296 75.0 4.00 0 0.4100 5.888 47.6
297 0.0 6.91 0 0.4480 5.682 33.8
298 0.0 18.10 0 0.7400 6.219 100.0
299 0.0 8.56 0 0.5200 5.836 91.9
300 95.0 1.47 0 0.4030 6.975 15.3
301 0.0 25.65 0 0.5810 5.961 92.9
302 0.0 9.69 0 0.5850 5.926 42.6
303 60.0 2.93 0 0.4010 6.800 9.9
304 0.0 8.56 0 0.5200 6.167 90.0
305 0.0 18.10 0 0.5840 5.565 70.6
306 0.0 18.10 0 0.6680 4.906 100.0
307 0.0 10.01 0 0.5470 5.913 92.9
308 0.0 18.10 0 0.7130 6.513 89.9
309 52.5 5.32 0 0.4050 6.565 22.9
310 0.0 21.89 0 0.6240 5.693 96.0
311 0.0 19.58 0 0.6050 5.880 97.3
312 80.0 3.37 0 0.3980 5.787 31.1
313 0.0 4.05 0 0.5100 6.315 73.4
314 0.0 9.90 0 0.5440 6.382 67.2
315 0.0 9.90 0 0.5440 6.567 87.3
316 0.0 6.91 0 0.4480 6.211 6.5
317 0.0 5.19 0 0.5150 5.869 46.3
318 0.0 27.74 0 0.6090 5.093 98.0
319 0.0 9.90 0 0.5440 6.266 82.8
320 0.0 27.74 0 0.6090 5.983 83.5
321 0.0 4.05 0 0.5100 5.859 68.7
322 0.0 8.14 0 0.5380 5.456 36.6
323 0.0 13.92 0 0.4370 5.790 58.0
324 85.0 0.74 0 0.4100 6.383 35.7
325 52.5 5.32 0 0.4050 6.209 31.3
326 0.0 18.10 0 0.5840 5.837 59.7
327 0.0 18.10 0 0.7180 6.824 76.5
328 40.0 6.41 1 0.4470 6.826 27.6
329 60.0 2.93 0 0.4010 6.604 18.8
330 0.0 18.10 0 0.6710 6.794 98.8
331 0.0 1.89 0 0.5180 6.540 59.7
332 33.0 2.18 0 0.4720 6.849 70.3
333 0.0 18.10 0 0.6930 5.747 98.9
334 0.0 10.01 0 0.5470 5.731 65.2
335 0.0 18.10 0 0.6930 6.471 98.8
336 0.0 21.89 0 0.6240 6.458 98.9
337 22.0 5.86 0 0.4310 5.593 76.5
338 0.0 18.10 0 0.6790 6.202 78.7
339 0.0 2.18 0 0.4580 7.147 54.2
340 0.0 25.65 0 0.5810 5.879 95.8
341 0.0 6.20 1 0.5070 6.631 76.5
342 20.0 3.97 0 0.6470 7.206 91.6
343 0.0 4.49 0 0.4490 6.389 48.0
344 0.0 19.58 0 0.6050 5.854 91.8
345 0.0 13.89 1 0.5500 5.888 56.0
346 0.0 18.10 0 0.7400 6.251 96.6
347 0.0 9.90 0 0.5440 6.122 52.8
348 0.0 18.10 0 0.6930 5.987 100.0
349 90.0 1.22 0 0.4030 7.249 21.9
350 0.0 18.10 0 0.6140 6.103 85.1
351 45.0 3.44 0 0.4370 6.556 29.1
352 0.0 2.18 0 0.4580 6.430 58.7
353 0.0 8.56 0 0.5200 6.137 87.4
354 0.0 18.10 0 0.7000 6.051 82.5
355 0.0 19.58 0 0.8710 5.597 94.9
356 0.0 6.20 0 0.5070 6.086 61.5
357 0.0 8.14 0 0.5380 5.701 95.0
358 12.5 7.87 0 0.5240 6.012 66.6
359 0.0 25.65 0 0.5810 5.613 95.6
360 0.0 6.91 0 0.4480 6.069 40.0
361 20.0 3.97 0 0.6470 7.014 84.6
362 0.0 10.59 0 0.4890 5.783 72.7
363 0.0 2.46 0 0.4880 7.155 92.2
364 55.0 2.25 0 0.3890 6.453 31.9
365 20.0 3.97 0 0.6470 7.333 100.0
366 0.0 2.89 0 0.4450 7.820 36.9
367 82.5 2.03 0 0.4150 6.162 38.4
368 0.0 8.14 0 0.5380 5.935 29.3
369 80.0 3.37 0 0.3980 6.290 17.8
370 0.0 5.96 0 0.4990 5.841 61.4
371 0.0 18.10 0 0.7130 6.376 88.4
372 90.0 2.97 0 0.4000 7.088 20.8
373 12.5 7.87 0 0.5240 6.377 94.3
374 75.0 2.95 0 0.4280 7.024 15.8
375 20.0 3.33 1 0.4429 7.645 49.7
376 0.0 18.10 0 0.7700 5.362 96.2
377 0.0 6.20 0 0.5040 8.266 78.3
378 20.0 3.97 0 0.6470 5.560 62.8
379 0.0 18.10 1 0.7700 6.212 97.4
380 22.0 5.86 0 0.4310 6.226 79.2
381 0.0 25.65 0 0.5810 5.870 69.7
382 0.0 18.10 0 0.7400 5.854 96.6
383 22.0 5.86 0 0.4310 8.259 8.4
384 0.0 18.10 0 0.6930 5.683 100.0
385 0.0 4.05 0 0.5100 6.546 33.1
386 0.0 9.90 0 0.5440 5.914 83.2
387 30.0 4.93 0 0.4280 6.606 42.2
388 22.0 5.86 0 0.4310 6.438 8.9
389 0.0 9.69 0 0.5850 5.390 72.9
390 22.0 5.86 0 0.4310 6.108 34.9
391 0.0 8.14 0 0.5380 5.834 56.5
392 0.0 8.14 0 0.5380 5.570 98.1
393 35.0 6.06 0 0.4379 5.706 28.4
394 0.0 18.10 0 0.7130 6.317 83.0
395 0.0 18.10 0 0.7000 5.390 98.9
396 45.0 3.44 0 0.4370 6.782 41.1
397 0.0 10.59 0 0.4890 5.891 22.3
398 0.0 3.41 0 0.4890 6.417 66.1
399 0.0 10.01 0 0.5470 6.715 81.6
400 0.0 19.58 0 0.6050 6.101 93.0
401 80.0 1.52 0 0.4040 7.274 38.3
402 0.0 18.10 0 0.7400 6.485 100.0
403 0.0 19.58 1 0.6050 6.250 92.6
404 0.0 4.05 0 0.5100 5.572 88.5
405 0.0 19.58 0 0.8710 5.272 94.0
406 0.0 7.38 0 0.4930 6.415 40.1
407 0.0 7.38 0 0.4930 6.376 54.3
408 0.0 7.07 0 0.4690 7.185 61.1
409 0.0 18.10 0 0.5800 5.926 71.0
410 0.0 7.07 0 0.4690 6.421 78.9
411 0.0 18.10 0 0.6790 6.193 78.1
412 0.0 10.59 0 0.4890 5.412 9.8
413 20.0 6.96 1 0.4640 7.691 51.8
414 33.0 2.18 0 0.4720 6.616 58.1
415 0.0 8.14 0 0.5380 6.142 91.7
416 20.0 3.97 0 0.6470 7.520 89.4
417 0.0 18.10 0 0.7180 4.963 91.4
418 0.0 4.49 0 0.4490 6.015 45.1
419 0.0 18.10 0 0.5830 5.871 41.9
420 0.0 18.10 0 0.5970 4.628 100.0
421 0.0 8.56 0 0.5200 6.127 85.2
422 0.0 18.10 0 0.6680 4.138 100.0
423 0.0 10.59 1 0.4890 5.404 88.6
424 0.0 9.69 0 0.5850 5.569 73.5
425 0.0 12.83 0 0.4370 6.273 6.0
426 0.0 4.39 0 0.4420 5.898 52.3
427 0.0 21.89 0 0.6240 5.637 94.7
428 0.0 18.10 0 0.6310 3.863 100.0
429 0.0 21.89 0 0.6240 6.174 93.6
430 0.0 13.89 1 0.5500 5.951 93.8
431 0.0 18.10 0 0.6790 5.304 89.1
432 0.0 10.59 1 0.4890 5.344 100.0
433 0.0 8.14 0 0.5380 6.495 94.4
434 70.0 2.24 0 0.4000 6.871 47.4
435 0.0 18.10 0 0.5320 7.061 77.0
436 40.0 6.41 1 0.4470 6.758 32.9
437 45.0 3.44 0 0.4370 6.951 21.5
438 0.0 6.20 0 0.5040 6.552 21.4
439 0.0 19.58 0 0.8710 6.130 100.0
440 33.0 2.18 0 0.4720 7.420 71.9
441 35.0 1.52 0 0.4420 7.241 49.3
442 0.0 21.89 0 0.6240 6.151 97.9
443 0.0 21.89 0 0.6240 6.335 98.2
444 0.0 12.83 0 0.4370 6.140 45.8
445 0.0 2.89 0 0.4450 6.163 69.6
446 0.0 6.20 0 0.5070 8.247 70.4
447 0.0 18.10 0 0.7130 5.976 87.9
448 0.0 9.90 0 0.5440 4.973 37.8
449 0.0 7.38 0 0.4930 6.426 52.3
450 0.0 8.56 0 0.5200 6.195 54.4
451 18.0 2.31 0 0.5380 6.575 65.2
452 0.0 18.10 0 0.7130 6.297 91.8
453 0.0 5.19 0 0.5150 6.059 37.3
454 0.0 8.56 0 0.5200 6.405 85.4
455 0.0 5.19 0 0.5150 6.037 34.5
456 20.0 3.33 0 0.4429 6.968 37.2
457 0.0 21.89 0 0.6240 6.454 98.4
458 0.0 7.38 0 0.4930 6.041 49.9
459 0.0 12.83 0 0.4370 6.286 45.0
460 0.0 18.10 0 0.7000 5.000 89.5
461 0.0 5.96 0 0.4990 5.850 41.5
462 25.0 4.86 0 0.4260 6.167 46.7
463 25.0 4.86 0 0.4260 6.619 70.4
464 30.0 4.93 0 0.4280 6.358 52.9
465 20.0 6.96 0 0.4640 5.856 42.1
466 0.0 18.10 0 0.5970 5.617 97.9
467 0.0 18.10 0 0.7000 5.277 98.1
468 0.0 7.38 0 0.4930 6.431 14.7
469 0.0 25.65 0 0.5810 6.004 84.1
470 0.0 6.20 0 0.5040 7.686 17.0
471 80.0 3.64 0 0.3920 5.876 19.1
472 0.0 18.10 0 0.7400 6.629 94.6
473 85.0 4.15 0 0.4290 6.516 27.7
474 0.0 6.20 1 0.5070 6.879 77.7
475 25.0 4.86 0 0.4260 6.727 33.5
476 0.0 19.58 0 0.6050 6.319 96.1
477 0.0 2.46 0 0.4880 6.563 95.6
478 0.0 11.93 0 0.5730 6.976 91.0
479 0.0 10.59 1 0.4890 6.064 59.1
480 21.0 5.64 0 0.4390 5.998 21.4
employ_dist radial_access tax_rate pupil_teacher_ratio lower_stat_pct
1 1.9669 4 437 21.2 16.90
2 4.8122 5 224 20.2 9.29
3 1.5331 24 666 20.2 24.56
4 2.2060 24 666 20.2 24.10
5 2.4961 24 666 20.2 18.71
6 5.2873 4 305 19.2 9.88
7 2.4527 24 666 20.2 16.74
8 1.8206 24 666 20.2 34.02
9 3.4211 5 276 16.4 9.69
10 10.5857 4 334 22.0 8.05
11 4.6947 5 216 14.9 3.76
12 1.3216 5 403 14.7 26.82
13 2.0651 24 666 20.2 19.52
14 3.0334 24 666 20.2 16.29
15 2.3889 1 273 21.0 6.48
16 2.3158 24 666 20.2 16.22
17 4.0952 4 307 21.0 19.88
18 5.4509 5 311 15.2 15.71
19 2.2955 24 666 20.2 14.19
20 1.2852 24 666 20.2 12.13
21 2.5451 5 384 20.9 15.55
22 3.9175 3 223 18.6 7.73
23 6.1899 6 300 16.6 6.36
24 1.7494 5 403 14.7 15.12
25 10.7103 4 411 18.3 7.79
26 8.3440 4 280 17.0 5.99
27 5.9604 4 289 16.0 6.27
28 1.9512 24 666 20.2 13.11
29 1.7700 24 666 20.2 25.68
30 3.0921 2 270 17.8 8.20
31 6.0877 3 233 17.9 16.20
32 1.4165 24 666 20.2 17.21
33 1.6232 5 403 14.7 15.79
34 2.7778 5 384 20.9 9.42
35 1.7912 24 666 20.2 15.17
36 3.9900 4 307 21.0 27.71
37 2.3460 4 437 21.2 17.31
38 2.5329 24 666 20.2 11.66
39 2.1850 24 666 20.2 19.31
40 1.8773 5 403 14.7 4.59
41 6.4584 5 224 20.2 6.75
42 3.6659 4 270 18.2 8.16
43 2.5480 6 432 17.8 17.09
44 3.7476 3 247 18.5 8.44
45 4.0317 4 304 18.4 15.94
46 3.2797 3 193 17.8 13.15
47 2.7831 24 666 20.2 16.23
48 7.8265 7 330 19.1 6.56
49 7.8278 5 358 14.8 4.97
50 4.7872 4 254 17.6 6.05
51 8.0136 3 352 18.8 10.53
52 2.2004 24 666 20.2 12.03
53 2.3817 6 391 19.2 12.01
54 2.3053 24 666 20.2 18.68
55 2.5052 24 666 20.2 13.27
56 5.7209 3 233 17.9 4.84
57 6.4654 5 370 17.6 4.61
58 1.8209 4 711 20.1 18.06
59 2.4786 1 273 21.0 9.67
60 1.3449 24 666 20.2 23.24
61 3.2721 8 307 17.4 7.60
62 4.3549 4 277 18.6 10.97
63 1.4118 5 403 14.7 26.42
64 1.5895 24 666 20.2 30.62
65 4.4546 4 307 21.0 16.51
66 10.5857 4 334 22.0 5.57
67 3.9454 4 277 18.6 9.47
68 5.6894 3 233 17.9 18.80
69 1.6180 5 403 14.7 14.10
70 4.4619 4 307 21.0 10.26
71 1.5192 24 666 20.2 21.08
72 1.7554 4 711 20.1 23.97
73 5.4159 5 287 19.6 12.79
74 2.2710 4 437 21.2 12.26
75 3.6519 8 307 17.4 8.05
76 4.1007 5 216 14.9 4.85
77 3.7598 4 307 21.0 20.34
78 4.2673 4 254 17.6 2.98
79 5.2873 4 305 19.2 7.54
80 8.3248 5 256 15.1 3.95
81 2.4259 5 403 14.7 14.43
82 1.8347 24 666 20.2 29.05
83 2.7986 6 391 19.2 17.60
84 4.2330 4 307 21.0 22.60
85 1.4655 24 666 20.2 19.78
86 2.8965 24 666 20.2 14.36
87 1.8589 24 666 20.2 15.02
88 2.4329 5 384 20.9 12.27
89 3.8473 5 279 19.2 10.13
90 2.7474 6 432 17.8 10.30
91 3.4217 2 270 17.8 5.50
92 4.2579 4 307 21.0 14.67
93 1.6390 24 666 20.2 20.31
94 2.3236 24 666 20.2 17.44
95 1.8746 24 666 20.2 15.70
96 2.2885 5 264 13.0 5.91
97 7.3090 2 329 12.6 8.61
98 6.3361 3 244 15.9 3.11
99 2.4358 24 666 20.2 18.13
100 3.4145 2 270 17.8 5.70
101 6.3361 6 300 16.6 12.40
102 2.5403 24 666 20.2 21.32
103 2.7227 24 666 20.2 11.48
104 2.1007 24 666 20.2 24.91
105 2.0107 5 264 13.0 6.90
106 1.5275 24 666 20.2 21.22
107 6.0622 3 222 18.7 2.94
108 7.2255 8 284 19.7 6.73
109 3.7965 4 307 21.0 11.28
110 1.4896 24 666 20.2 30.59
111 2.5091 24 666 20.2 12.67
112 1.6132 24 666 20.2 7.12
113 12.1265 5 187 17.0 4.50
114 3.6715 8 307 17.4 5.25
115 7.8265 7 330 19.1 9.52
116 2.8290 3 193 17.8 5.04
117 3.6659 4 270 18.2 6.21
118 1.4608 5 403 14.7 29.53
119 2.5975 24 666 20.2 16.42
120 4.7211 5 287 19.6 11.74
121 2.0882 24 666 20.2 19.69
122 4.6820 4 307 21.0 14.81
123 4.5026 5 398 18.7 9.10
124 2.5050 1 273 21.0 7.88
125 3.9454 4 277 18.6 9.38
126 3.3175 4 304 18.4 4.54
127 2.4775 6 432 17.8 15.37
128 2.4982 6 391 19.2 14.33
129 2.5182 24 666 20.2 7.79
130 4.3996 4 307 21.0 16.30
131 3.4952 2 276 18.0 6.19
132 7.8148 8 284 19.7 6.86
133 2.8340 4 304 18.4 11.72
134 6.8147 4 243 16.8 13.45
135 7.3172 6 293 16.6 7.60
136 1.1781 24 666 20.2 23.34
137 1.9047 24 666 20.2 14.64
138 4.4290 3 223 18.6 6.59
139 5.4011 3 252 18.3 4.32
140 1.9047 24 666 20.2 5.29
141 3.8771 4 277 18.6 17.27
142 1.7659 5 403 14.7 7.39
143 6.4980 4 345 18.9 8.10
144 5.1167 4 245 19.2 3.33
145 2.7301 6 432 17.8 12.04
146 2.2875 1 273 21.0 9.08
147 3.5549 5 296 16.6 10.11
148 1.9142 24 666 20.2 26.45
149 1.3459 5 403 14.7 29.29
150 3.1025 4 304 18.4 9.97
151 5.1167 4 245 19.2 3.56
152 1.8125 4 437 21.2 15.39
153 2.3552 24 666 20.2 17.73
154 1.4394 4 437 21.2 34.41
155 1.9444 2 188 19.1 25.41
156 1.8026 24 666 20.2 20.62
157 2.8561 5 384 20.9 7.67
158 8.9067 7 330 19.1 3.53
159 6.6407 1 304 16.9 7.83
160 2.1705 24 666 20.2 18.03
161 3.9450 4 304 18.4 11.50
162 5.5027 4 289 16.0 10.40
163 5.8700 3 233 17.9 30.81
164 1.5741 24 666 20.2 20.32
165 1.7455 5 403 14.7 15.02
166 2.8715 24 666 20.2 17.15
167 4.1480 8 307 17.4 4.73
168 1.8662 24 666 20.2 22.11
169 5.5027 4 289 16.0 8.58
170 6.9320 8 284 19.7 9.22
171 3.6526 4 277 18.6 16.03
172 3.2157 8 307 17.4 3.13
173 7.9809 8 284 19.7 9.50
174 1.3861 24 666 20.2 23.69
175 2.3274 4 437 21.2 11.12
176 2.5979 3 193 17.8 9.45
177 10.7103 4 411 18.3 5.49
178 4.7075 4 307 21.0 8.26
179 6.4980 4 345 18.9 13.09
180 6.4980 4 345 18.9 8.79
181 3.0993 24 666 20.2 12.87
182 3.6150 4 270 18.2 10.59
183 1.5296 5 403 14.7 28.32
184 7.6534 3 402 17.0 4.45
185 1.9512 24 666 20.2 14.10
186 5.9505 5 311 15.2 19.15
187 3.2157 8 307 17.4 6.36
188 5.9604 4 289 16.0 7.39
189 3.3633 5 276 16.4 10.50
190 4.8122 5 224 20.2 9.74
191 1.6074 24 666 20.2 27.38
192 4.0123 4 307 21.0 13.83
193 1.7573 5 403 14.7 6.43
194 1.8172 24 666 20.2 22.88
195 5.4917 7 329 16.1 9.50
196 1.5916 5 403 14.7 13.28
197 5.2873 4 305 19.2 6.72
198 1.4395 24 666 20.2 30.63
199 7.3090 2 329 12.6 4.08
200 2.4699 4 437 21.2 15.03
201 1.9096 24 666 20.2 24.39
202 1.5004 24 666 20.2 29.97
203 3.8384 8 307 17.4 2.47
204 1.7821 24 666 20.2 16.35
205 2.9879 3 193 17.8 13.98
206 6.8147 4 243 16.8 5.28
207 3.4952 2 276 18.0 6.65
208 2.1069 5 384 20.9 16.47
209 5.7321 5 370 17.6 7.18
210 3.6715 8 307 17.4 11.65
211 3.0480 8 307 17.4 21.46
212 4.1750 4 307 21.0 13.04
213 1.8681 4 711 20.1 18.07
214 2.0527 24 666 20.2 17.64
215 6.2267 5 311 15.2 13.27
216 8.7921 1 335 19.7 5.98
217 5.4917 7 329 16.1 8.67
218 5.4159 5 287 19.6 6.15
219 1.6102 5 403 14.7 12.12
220 1.6686 4 437 21.2 21.32
221 1.9682 24 666 20.2 24.08
222 4.1403 4 254 17.6 7.19
223 2.9634 24 666 20.2 13.22
224 3.0665 24 666 20.2 14.13
225 3.4952 2 276 18.0 4.21
226 9.2229 3 216 18.6 8.05
227 3.3317 24 666 20.2 7.74
228 4.0983 24 666 20.2 10.42
229 2.8720 5 264 13.0 3.16
230 1.4130 24 666 20.2 10.11
231 3.4242 24 666 20.2 10.74
232 1.5166 5 403 14.7 16.65
233 1.9879 24 666 20.2 23.98
234 5.1180 4 224 14.7 3.81
235 2.2625 5 403 14.7 11.32
236 5.2146 4 430 16.9 9.97
237 6.8185 8 284 19.7 14.44
238 5.1004 3 233 17.9 14.15
239 2.7792 24 666 20.2 16.94
240 2.6463 5 296 16.6 9.04
241 7.9549 7 330 19.1 18.46
242 3.1523 24 666 20.2 11.45
243 3.9175 3 223 18.6 13.65
244 2.7410 3 193 17.8 7.56
245 3.3603 5 279 19.2 9.68
246 2.8617 8 307 17.4 9.71
247 2.2565 6 432 17.8 10.45
248 5.6150 5 224 20.2 10.56
249 5.7209 3 233 17.9 5.81
250 4.4534 4 307 21.0 17.28
251 1.3861 24 666 20.2 21.78
252 4.5667 5 398 15.2 5.39
253 2.7175 24 666 20.2 14.70
254 6.8147 4 243 16.8 9.43
255 2.2222 24 666 20.2 15.17
256 4.2390 4 307 21.0 11.98
257 2.9153 5 296 16.6 6.92
258 1.6768 24 666 20.2 19.37
259 2.1121 5 264 13.0 9.59
260 2.0788 5 264 13.0 11.25
261 7.3967 7 330 19.1 5.90
262 1.3163 24 666 20.2 13.44
263 9.2203 1 315 16.4 6.57
264 5.2873 4 305 19.2 5.52
265 2.4631 6 432 17.8 15.76
266 1.6582 24 666 20.2 36.98
267 7.2254 8 284 19.7 13.15
268 1.5804 24 666 20.2 23.60
269 3.9917 24 666 20.2 10.58
270 6.5921 5 311 15.2 17.10
271 2.0026 24 666 20.2 18.05
272 5.2146 4 430 16.9 7.34
273 4.0522 5 398 18.7 11.97
274 1.4672 24 666 20.2 28.28
275 8.7921 1 335 19.7 5.89
276 3.5459 24 666 20.2 14.98
277 1.5894 24 666 20.2 20.08
278 7.8278 5 358 14.8 4.74
279 6.4798 5 398 15.2 2.87
280 2.8927 6 391 19.2 14.10
281 2.0720 24 666 20.2 17.79
282 1.8195 24 666 20.2 25.79
283 2.2616 24 666 20.2 18.13
284 7.0355 6 300 16.6 5.19
285 4.4377 3 247 18.5 6.53
286 5.1167 4 245 19.2 4.70
287 2.4298 24 666 20.2 18.14
288 5.4007 4 281 19.0 6.72
289 2.8237 24 666 20.2 14.76
290 2.4259 5 403 14.7 12.14
291 5.4917 7 329 16.1 4.86
292 1.9265 24 666 20.2 17.11
293 5.0141 5 398 18.7 12.34
294 1.9929 2 188 19.1 14.81
295 2.4091 6 391 19.2 12.92
296 7.3197 3 469 21.1 14.80
297 5.1004 3 233 17.9 10.21
298 2.0048 24 666 20.2 16.59
299 2.2110 5 384 20.9 18.66
300 7.6534 3 402 17.0 4.56
301 2.0869 2 188 19.1 17.93
302 2.3817 6 391 19.2 13.59
303 6.2196 1 265 15.6 5.03
304 2.4210 5 384 20.9 12.33
305 2.0635 24 666 20.2 17.16
306 1.1742 24 666 20.2 34.77
307 2.3534 6 432 17.8 16.21
308 2.8016 24 666 20.2 10.29
309 7.3172 6 293 16.6 9.51
310 1.7883 4 437 21.2 17.19
311 2.3887 5 403 14.7 12.03
312 6.6115 4 337 16.1 10.24
313 3.3175 5 296 16.6 6.29
314 3.5325 4 304 18.4 10.36
315 3.6023 4 304 18.4 9.28
316 5.7209 3 233 17.9 7.44
317 5.2311 5 224 20.2 9.80
318 1.8226 4 711 20.1 29.68
319 3.2628 4 304 18.4 7.90
320 2.1099 4 711 20.1 13.35
321 2.7019 5 296 16.6 9.64
322 3.7965 4 307 21.0 11.69
323 6.3200 4 289 16.0 15.84
324 9.1876 2 313 17.3 5.77
325 7.3172 6 293 16.6 7.14
326 1.9976 24 666 20.2 15.69
327 1.7940 24 666 20.2 22.74
328 4.8628 4 254 17.6 4.16
329 6.2196 1 265 15.6 4.38
330 1.3580 24 666 20.2 21.24
331 6.2669 1 422 15.9 8.65
332 3.1827 7 222 18.4 7.53
333 1.6334 24 666 20.2 19.92
334 2.7592 6 432 17.8 13.61
335 1.7257 24 666 20.2 17.12
336 2.1185 4 437 21.2 12.60
337 7.9549 7 330 19.1 12.50
338 1.8629 24 666 20.2 14.52
339 6.0622 3 222 18.7 5.33
340 2.0063 2 188 19.1 17.58
341 4.1480 8 307 17.4 9.54
342 1.9301 5 264 13.0 8.10
343 4.7794 3 247 18.5 9.62
344 2.4220 5 403 14.7 11.64
345 3.1121 5 276 16.4 13.51
346 2.1980 24 666 20.2 16.44
347 2.6403 4 304 18.4 5.98
348 1.5888 24 666 20.2 26.77
349 8.6966 5 226 17.9 4.81
350 2.0218 24 666 20.2 23.29
351 4.5667 5 398 15.2 4.56
352 6.0622 3 222 18.7 5.21
353 2.7147 5 384 20.9 13.44
354 2.1678 24 666 20.2 18.76
355 1.5257 5 403 14.7 21.45
356 3.6519 8 307 17.4 10.88
357 3.7872 4 307 21.0 18.35
358 5.5605 5 311 15.2 12.43
359 1.7572 2 188 19.1 27.26
360 5.7209 3 233 17.9 9.55
361 2.1329 5 264 13.0 14.79
362 4.3549 4 277 18.6 18.06
363 2.7006 3 193 17.8 4.82
364 7.3073 1 300 15.3 8.23
365 1.8946 5 264 13.0 7.79
366 3.4952 2 276 18.0 3.57
367 6.2700 2 348 14.7 7.43
368 4.4986 4 307 21.0 6.58
369 6.6115 4 337 16.1 4.67
370 3.3779 5 279 19.2 11.41
371 2.5671 24 666 20.2 14.65
372 7.3073 1 285 15.3 7.85
373 6.3467 5 311 15.2 20.45
374 5.4011 3 252 18.3 1.98
375 5.2119 5 216 14.9 3.01
376 2.1036 24 666 20.2 10.19
377 2.8944 8 307 17.4 4.14
378 1.9865 5 264 13.0 10.45
379 2.1222 24 666 20.2 17.60
380 8.0555 7 330 19.1 10.15
381 2.2577 2 188 19.1 14.37
382 1.8956 24 666 20.2 23.79
383 8.9067 7 330 19.1 3.54
384 1.4254 24 666 20.2 22.98
385 3.1323 5 296 16.6 5.33
386 3.9986 4 304 18.4 18.33
387 6.1899 6 300 16.6 7.37
388 7.3967 7 330 19.1 3.59
389 2.7986 6 391 19.2 21.14
390 8.0555 7 330 19.1 9.16
391 4.4986 4 307 21.0 8.47
392 3.7979 4 307 21.0 21.02
393 6.6407 1 304 16.9 12.43
394 2.7344 24 666 20.2 13.99
395 1.7281 24 666 20.2 20.85
396 3.7886 5 398 15.2 6.68
397 3.9454 4 277 18.6 10.87
398 3.0923 2 270 17.8 8.81
399 2.6775 6 432 17.8 10.16
400 2.2834 5 403 14.7 9.81
401 7.3090 2 329 12.6 6.62
402 1.9784 24 666 20.2 18.85
403 1.7984 5 403 14.7 5.50
404 2.5961 5 296 16.6 14.69
405 1.7364 5 403 14.7 16.14
406 4.7211 5 287 19.6 6.12
407 4.5404 5 287 19.6 6.87
408 4.9671 2 242 17.8 4.03
409 2.9084 24 666 20.2 18.13
410 4.9671 2 242 17.8 9.14
411 1.9356 24 666 20.2 21.52
412 3.5875 4 277 18.6 29.55
413 4.3665 3 223 18.6 6.58
414 3.3700 7 222 18.4 8.93
415 3.9769 4 307 21.0 18.72
416 2.1398 5 264 13.0 7.26
417 1.7523 24 666 20.2 14.00
418 4.4272 3 247 18.5 12.86
419 3.7240 24 666 20.2 13.34
420 1.5539 24 666 20.2 34.37
421 2.1224 5 384 20.9 14.09
422 1.1370 24 666 20.2 37.97
423 3.6650 4 277 18.6 23.98
424 2.3999 6 391 19.2 15.10
425 4.2515 5 398 18.7 6.78
426 8.0136 3 352 18.8 12.67
427 1.9799 4 437 21.2 18.34
428 1.5106 24 666 20.2 13.33
429 1.6119 4 437 21.2 24.16
430 2.8893 5 276 16.4 17.92
431 1.6475 24 666 20.2 26.64
432 3.8750 4 277 18.6 23.09
433 4.4547 4 307 21.0 12.80
434 7.8278 5 358 14.8 6.07
435 3.4106 24 666 20.2 7.01
436 4.0776 4 254 17.6 3.53
437 6.4798 5 398 15.2 5.10
438 3.3751 8 307 17.4 3.76
439 1.4191 5 403 14.7 27.80
440 3.0992 7 222 18.4 6.47
441 7.0379 1 284 15.5 5.49
442 1.6687 4 437 21.2 18.46
443 2.1107 4 437 21.2 16.96
444 4.0905 5 398 18.7 10.27
445 3.4952 2 276 18.0 11.34
446 3.6519 8 307 17.4 3.95
447 2.5806 24 666 20.2 19.01
448 2.5194 4 304 18.4 12.64
449 4.5404 5 287 19.6 7.20
450 2.7778 5 384 20.9 13.00
451 4.0900 1 296 15.3 4.98
452 2.3682 24 666 20.2 17.27
453 4.8122 5 224 20.2 8.51
454 2.7147 5 384 20.9 10.63
455 5.9853 5 224 20.2 8.01
456 5.2447 5 216 14.9 4.59
457 1.8498 4 437 21.2 14.59
458 4.7211 5 287 19.6 7.70
459 4.5026 5 398 18.7 8.94
460 1.5184 24 666 20.2 31.99
461 3.9342 5 279 19.2 8.77
462 5.4007 4 281 19.0 7.51
463 5.4007 4 281 19.0 7.22
464 7.0355 6 300 16.6 11.22
465 4.4290 3 223 18.6 13.00
466 1.4547 24 666 20.2 26.40
467 1.4261 24 666 20.2 30.81
468 5.4159 5 287 19.6 5.08
469 2.1974 2 188 19.1 14.27
470 3.3751 8 307 17.4 3.92
471 9.2203 1 315 16.4 9.25
472 2.1247 24 666 20.2 23.27
473 8.5353 4 351 17.9 6.36
474 3.2721 8 307 17.4 9.93
475 5.4007 4 281 19.0 5.29
476 2.1000 5 403 14.7 11.10
477 2.8470 3 193 17.8 5.68
478 2.1675 1 273 21.0 5.64
479 4.2392 4 277 18.6 14.66
480 6.8147 4 243 16.8 8.43
dist_fenway_park
1 5238.4649
2 16343.7994
3 9473.0404
4 7692.1031
5 8409.6621
6 2137.6604
7 8835.9189
8 8054.4195
9 4516.7075
10 29213.9834
11 9566.9624
12 7084.5010
13 9099.9205
14 6940.3943
15 12995.2441
16 7983.2537
17 14045.1213
18 16346.2229
19 5065.9960
20 7843.3768
21 5323.0309
22 7284.0197
23 13337.6577
24 5835.2694
25 33638.4011
26 21633.5409
27 9090.1756
28 6435.7633
29 9729.6859
30 6668.9243
31 16140.2464
32 7087.6383
33 5983.7672
34 4744.0380
35 10393.9951
36 14170.6464
37 4496.0298
38 6444.8955
39 8423.2204
40 5292.7403
41 17193.7943
42 5432.6896
43 6851.6991
44 6813.9032
45 12732.5156
46 4606.8190
47 8469.5523
48 17732.6484
49 10193.8505
50 8170.8771
51 19445.1093
52 7693.4523
53 10106.2800
54 7300.8939
55 4855.8948
56 10388.8084
57 20045.9642
58 9616.0707
59 13067.0246
60 8276.9901
61 5311.2581
62 7199.5418
63 6734.6795
64 9696.1657
65 15004.5347
66 29566.6615
67 5245.9610
68 15771.8495
69 5921.5950
70 14107.7472
71 7563.3942
72 9238.2523
73 12666.7534
74 4570.8177
75 6137.9940
76 8030.5325
77 13599.9112
78 7713.2559
79 3474.0827
80 18695.8489
81 4350.3472
82 7936.1757
83 10210.6082
84 14703.3965
85 7675.5408
86 6377.6435
87 6626.0895
88 5841.4786
89 8859.4273
90 8239.0673
91 6607.4227
92 13459.7352
93 9192.3094
94 8261.2299
95 6336.4263
96 5501.0021
97 16111.5536
98 15760.8220
99 7968.9193
100 7404.1003
101 14530.5958
102 7004.9787
103 4935.8109
104 7455.4726
105 5520.5627
106 7441.3568
107 18940.0623
108 19571.4132
109 12414.0013
110 9642.9388
111 5052.2148
112 6543.8707
113 31656.0945
114 6533.1389
115 18589.1409
116 3822.5389
117 4326.1532
118 6459.4829
119 8706.4386
120 14756.0871
121 7734.2648
122 15462.4676
123 1649.4173
124 13555.9718
125 6004.6916
126 11572.6249
127 8293.6710
128 12355.3798
129 5218.1759
130 14435.3621
131 3420.6876
132 17671.9771
133 11220.3051
134 16286.7237
135 9906.7218
136 7935.6286
137 4890.9198
138 7617.2250
139 9456.3154
140 6516.7246
141 6929.6055
142 5742.0587
143 887.9007
144 9252.2245
145 7487.5091
146 12585.6906
147 3708.5164
148 8106.5137
149 6869.5930
150 10915.8069
151 8411.1472
152 5939.7786
153 8692.9900
154 6451.1144
155 7669.0097
156 7293.8084
157 4083.1474
158 19838.8447
159 14606.7235
160 7257.2983
161 14774.5334
162 8424.2491
163 15872.2869
164 9136.0534
165 5754.3366
166 7962.3354
167 7746.7201
168 9139.4539
169 8624.8190
170 17512.8200
171 5952.7918
172 5848.7851
173 21124.6041
174 7740.9521
175 4586.2048
176 4265.6108
177 31311.8977
178 14908.4883
179 1219.1524
180 1683.7868
181 6581.5293
182 4573.9695
183 6318.3127
184 18980.6047
185 6388.7543
186 17832.2322
187 5855.1010
188 9156.0464
189 4715.4110
190 16195.1959
191 8760.3656
192 13046.2223
193 5542.1011
194 8678.0120
195 9135.5190
196 5991.3141
197 2862.7360
198 9237.0239
199 13927.6780
200 4186.7061
201 6972.2592
202 8828.0972
203 7250.9033
204 10110.7373
205 4508.0908
206 15501.2481
207 3433.3156
208 5101.5766
209 19238.8280
210 6774.5257
211 5375.5246
212 15047.8081
213 9854.6118
214 7399.4823
215 17093.0247
216 30592.9190
217 9634.6127
218 13672.1089
219 6322.2493
220 5816.9151
221 7781.8443
222 7584.3011
223 8788.9281
224 8064.6388
225 2855.6581
226 26410.6025
227 6260.9940
228 6695.6112
229 5039.8596
230 7581.2102
231 6231.5406
232 6216.2745
233 8335.6144
234 12051.8258
235 4580.2701
236 11464.1463
237 18630.8459
238 14789.3535
239 8156.5825
240 3886.8531
241 19031.9496
242 7595.8202
243 6941.1962
244 4045.2572
245 11173.1712
246 4992.2983
247 7281.6420
248 17199.8000
249 10524.6468
250 15485.9714
251 7686.0208
252 6069.8522
253 8805.8037
254 14947.6319
255 8062.2839
256 16468.0031
257 3587.9753
258 9548.6701
259 5486.9077
260 5901.9554
261 16634.3068
262 8062.1112
263 22449.8060
264 3939.9613
265 7634.4679
266 8147.4289
267 18621.3382
268 9296.3644
269 7662.8123
270 18761.9700
271 8086.7301
272 10411.0769
273 2410.1839
274 9410.3095
275 28087.6861
276 7480.3086
277 7816.9277
278 10370.6334
279 4767.8149
280 11940.9719
281 8677.5186
282 7729.6003
283 8711.8125
284 16251.0737
285 5520.8964
286 7854.5197
287 7546.2759
288 3393.0937
289 7021.7540
290 4209.2310
291 8256.4009
292 10493.6501
293 2002.5327
294 8746.5086
295 10898.4570
296 8923.7167
297 13958.4593
298 9051.2783
299 5398.4562
300 16757.7086
301 7539.8803
302 10057.8554
303 6373.9386
304 4745.1510
305 7015.7237
306 8388.5783
307 6343.3426
308 9411.5763
309 11017.2644
310 6349.3144
311 4366.9466
312 3152.3162
313 3570.1469
314 12031.4858
315 12888.6235
316 13355.9132
317 15640.8449
318 9015.7568
319 12025.4885
320 8370.1403
321 3893.2824
322 11887.8725
323 9181.6403
324 18644.8783
325 11214.1275
326 7190.6333
327 6819.3281
328 8535.7841
329 7517.3084
330 7891.5023
331 21383.1730
332 10321.0481
333 9580.1005
334 9248.0261
335 9916.1187
336 4979.0162
337 17564.2345
338 7241.1912
339 19587.5651
340 8255.4436
341 7506.7589
342 5944.9225
343 6200.8293
344 4369.1618
345 4944.1547
346 8911.5802
347 11700.2876
348 8908.7407
349 15052.2791
350 6741.9622
351 5861.5894
352 20179.7201
353 4177.0965
354 11213.2652
355 6241.3712
356 6581.4851
357 13353.3583
358 18039.4036
359 8099.7890
360 14482.9883
361 6072.5372
362 7194.4639
363 4261.9265
364 15684.3670
365 5704.5868
366 2813.4930
367 14547.7925
368 12394.9368
369 5816.6830
370 10270.1090
371 9305.9577
372 12528.4756
373 18095.2412
374 7530.0840
375 10888.2425
376 5312.9113
377 5259.0329
378 6094.1946
379 4925.7356
380 17830.9959
381 8861.4477
382 8427.8481
383 21352.7781
384 8866.5031
385 3228.8684
386 14112.3139
387 13144.8176
388 17129.7775
389 11240.0788
390 19415.6071
391 13575.1346
392 12909.4077
393 13393.8772
394 9759.8588
395 9924.6558
396 4254.7987
397 6691.5642
398 7954.8444
399 6339.8857
400 4548.3673
401 14039.8979
402 8721.2027
403 5437.9799
404 3951.0116
405 5691.8703
406 14392.4764
407 13502.1315
408 18096.7701
409 7119.0384
410 16530.1637
411 7454.0070
412 5205.8784
413 7380.5340
414 8312.9514
415 13552.0341
416 5594.1433
417 7013.5593
418 7307.4067
419 7192.6924
420 7624.0807
421 5901.7874
422 8362.7801
423 6388.4373
424 11611.7816
425 2543.0284
426 18963.7459
427 5434.3751
428 7217.5147
429 6551.0858
430 4510.4554
431 7456.5456
432 6792.2107
433 15796.7687
434 9866.9153
435 6541.2136
436 7435.2752
437 3865.4572
438 6446.5728
439 6660.0582
440 8925.0890
441 24448.8585
442 6160.5469
443 4686.6483
444 3133.7288
445 2837.0093
446 6563.7117
447 8044.1832
448 10943.2247
449 12624.9018
450 5542.2478
451 16177.5143
452 9282.5164
453 16720.5551
454 3863.9409
455 16455.6330
456 11369.1309
457 5608.6548
458 14144.8881
459 2148.1446
460 9509.5254
461 10604.4329
462 4550.9748
463 3343.4777
464 14943.5273
465 7498.9021
466 7850.6513
467 9372.9214
468 13870.6762
469 8128.8589
470 5991.5091
471 20401.3624
472 8113.6098
473 21226.5956
474 5704.1426
475 3488.4907
476 4840.6591
477 4323.4338
478 12208.1097
479 7413.9896
480 13275.6229
suppressMessages (library (geosphere))suppressMessages (library (leaflet))
1. Kriminalitet i Boston
I detta avsnitt ska ni analysera kriminaliteten i Boston med hjälp av variabeln crime_rate.
💪 Uppgift 1.1
Vad kan man generellt säga om kriminaliteten i Boston 1970? Använd lämpliga figurer och mått för att ge en beskrivning.
Skriv svaret här. Vid behov skrivs matematiska symboler inom dollartecken, till exempel \(\overline{y} = \cfrac{\sum^{n}_{i=1} y_i}{n}\) . Koden skrivs i R-rutan nedanför.
suppressMessages (library (mosaic))favstats (~ crime_rate, data = Boston_census)
min Q1 median Q3 max mean sd n missing
0.00632 0.0829725 0.253715 3.681942 88.9762 3.66526 8.746156 480 0
bwplot (~ crime_rate, data = Boston_census)= Boston_census[,5 ]
[1] 0.32264 0.06151 7.99248 6.39312 9.51363 0.15876 8.24809 13.67810
[9] 0.07013 0.04301 0.03578 3.32105 9.72418 4.34879 0.10959 5.58107
[17] 0.98843 0.09378 3.83684 11.95110 0.26363 0.11460 0.08244 1.41385
[25] 0.07244 0.01501 0.12932 14.33370 11.57790 0.03932 0.21977 88.97620
[33] 2.14918 0.14866 8.64476 1.38799 0.97617 4.64689 7.52601 1.22358
[41] 0.03738 0.04203 0.22212 0.07151 0.24522 0.06047 7.75223 0.19073
[49] 0.06466 0.10469 0.03113 6.44405 0.17331 4.87141 3.84970 0.12744
[57] 0.03049 0.15086 0.06263 15.28800 0.61470 0.22969 4.09740 14.33370
[65] 0.84054 0.10659 0.19802 0.22927 1.65660 0.63796 15.87440 0.18337
[73] 0.24103 1.19294 0.44791 0.21038 1.61282 0.09604 0.19539 0.01432
[81] 1.20742 18.08460 0.17899 1.13081 14.43830 3.56868 11.08740 0.12802
[89] 0.17505 0.15098 0.05660 0.78420 9.59571 6.71772 7.02259 0.52014
[97] 0.04666 0.01538 4.75237 0.05302 0.10612 4.42228 5.20177 15.02340
[105] 0.65665 14.05070 0.03237 0.11027 0.72580 38.35180 4.26131 4.55587
[113] 0.01709 0.46296 0.16439 0.05780 0.02875 2.36862 4.81213 0.28392
[121] 10.06230 0.67191 0.08387 0.04741 0.14052 0.49298 0.13058 0.22438
[129] 4.54192 0.75026 0.06860 0.15445 0.26169 0.08873 0.04590 20.71620
[137] 4.22239 0.16211 0.02763 3.47428 0.17446 1.42502 0.05789 0.03502
[145] 0.13158 0.04527 0.07022 15.17720 2.77974 0.34940 0.07886 0.32543
[153] 5.44114 1.62864 0.15038 73.53410 0.11432 0.08221 0.03466 10.23300
[161] 0.25356 0.08199 0.25387 14.23620 3.53501 3.77498 0.51183 22.05110
[169] 0.08265 0.10328 0.21719 0.38214 0.12650 23.64820 0.59005 0.06888
[177] 0.07950 0.62976 0.13554 0.12816 4.03841 0.04294 2.33099 0.01778
[185] 12.04820 0.14455 0.41238 0.05372 0.11425 0.05497 41.52920 0.85204
[193] 1.34284 9.39063 0.03537 1.49632 0.08826 20.08490 0.04011 0.32982
[201] 15.86030 9.91655 0.57529 13.35980 0.08308 0.05360 0.12204 0.13262
[209] 0.02543 0.53700 0.40771 1.35472 0.10574 8.49213 0.11747 0.06211
[217] 0.09266 0.30347 1.12658 0.24980 9.33889 0.07978 7.83932 3.16360
[225] 0.12083 0.01951 5.70818 2.81838 0.54050 51.13580 5.82401 2.15505
[233] 10.67180 0.03510 2.44953 0.06617 0.17171 0.18836 8.20058 0.09178
[241] 0.19133 4.83567 0.09065 0.06588 0.06417 0.35809 0.14231 0.03041
[249] 0.14150 0.95577 17.86670 0.08370 6.80117 0.04337 13.91340 1.00245
[257] 0.06642 5.87205 0.54011 0.82526 0.14030 19.60910 0.04819 0.09164
[265] 0.17134 45.74610 0.14932 9.18702 3.67367 0.17004 14.42080 0.06724
[273] 0.10153 24.39380 0.02899 5.69175 28.65580 0.05561 0.08664 0.26838
[281] 6.28807 10.83420 9.32909 0.12757 0.05735 0.03615 8.05579 0.03659
[289] 13.07510 1.80028 0.10000 6.96215 0.05646 0.16902 0.23912 0.01360
[297] 0.17142 5.66637 0.17120 0.03150 0.09299 0.27957 0.02187 0.13960
[305] 8.79212 11.10810 0.12329 5.82115 0.04297 0.25915 2.31390 0.04379
[313] 0.05425 0.40202 0.36920 0.15936 0.03427 0.20746 0.26938 0.11132
[321] 0.08447 0.80271 0.14103 0.02055 0.03871 12.24720 11.81230 0.06127
[329] 0.01439 9.82349 0.02498 0.04932 7.67202 0.14476 8.71675 0.34006
[337] 0.20608 37.66190 0.06905 0.09849 0.52058 0.55007 0.05059 2.24236
[345] 0.04560 9.92485 0.79041 25.04610 0.01311 7.05042 0.12579 0.02985
[353] 0.21161 5.29305 2.73397 0.33045 1.15172 0.08829 0.38735 0.12269
[361] 0.78570 0.25199 0.09103 0.01096 0.66351 0.08187 0.03445 1.05393
[369] 0.03584 0.09744 3.69311 0.00906 0.22489 0.03359 0.06129 3.67822
[377] 0.31533 0.76162 8.98296 0.19657 0.06899 12.80230 0.36894 67.92080
[385] 0.06664 0.31827 0.09252 0.21409 0.28960 0.33983 0.62739 1.25179
[393] 0.05023 6.65492 8.15174 0.07875 0.13642 0.04684 0.10084 2.92400
[401] 0.03768 9.96654 1.27346 0.13914 2.44668 0.34109 0.18159 0.02729
[409] 15.57570 0.02731 7.36711 0.28955 0.22188 0.05479 1.23247 0.53412
[417] 3.69695 0.05188 2.37857 18.81100 0.13117 18.49820 0.37578 0.17783
[425] 0.07896 0.06162 0.88125 13.52220 0.29090 0.11069 25.94060 0.43571
[433] 0.77299 0.04417 5.73116 0.05644 0.09068 0.44178 2.37934 0.07503
[441] 0.01301 0.54452 0.55778 0.08707 0.11504 0.33147 4.66883 2.63548
[449] 0.16760 0.10793 0.00632 5.09017 0.03306 0.22876 0.03961 0.03705
[457] 0.35233 0.35114 0.09512 22.59710 0.08014 0.03551 0.04462 0.10290
[465] 0.29916 7.40389 16.81180 0.19186 0.07165 0.29819 0.03548 11.16040
[473] 0.01870 0.62356 0.04113 2.30040 0.10008 0.06076 0.13587 0.04981
<- log (Crime_rate)
[1] -1.131218128 -2.788555516 2.078501100 1.855222412 2.252725507
[6] -1.840361651 2.109981658 2.615796014 -2.657404616 -3.146322632
[11] -3.330366201 1.200280998 2.274615567 1.469897645 -2.211009150
[16] 1.719380514 -0.011637453 -2.366803665 1.344649111 2.480823324
[21] -1.333208674 -2.166307475 -2.495684523 0.346316480 -2.624996646
[26] -4.199038633 -2.045465326 2.662613408 2.449098109 -3.236021984
[31] -1.515173734 4.488368918 0.765086374 -1.906093460 2.156953357
[36] 0.327856657 -0.024118527 1.536198179 2.018365021 0.201780988
[41] -3.286619477 -3.169371630 -1.504537503 -2.637917979 -1.405599512
[46] -2.805607905 2.047980544 -1.656896464 -2.738612507 -2.256751677
[51] -3.469583295 1.863157224 -1.752673381 1.583383423 1.347995223
[56] -2.060109613 -3.490356518 -1.891403025 -2.770510882 2.727068207
[61] -0.486620935 -1.471024705 1.410352626 2.662613408 -0.173710737
[66] -2.238765580 -1.619387243 -1.472854931 0.504767309 -0.449479694
[71] 2.764707749 -1.696249309 -1.422833872 0.176420848 -0.803162960
[76] -1.558839860 0.477984200 -2.342990508 -1.632757718 -4.246098117
[81] 0.188485852 2.895060747 -1.720425341 0.122934190 2.669884398
[86] 1.272195780 2.405809328 -2.055568777 -1.742683632 -1.890607901
[91] -2.871746294 -0.243091189 2.261316124 1.904748811 1.949132096
[96] -0.653657273 -3.064868012 -4.174687315 1.558643441 -2.937086078
[101] -2.243184750 1.486655400 1.648998952 2.709608986 -0.420604127
[106] 2.642672217 -3.430523211 -2.204823375 -0.320480784 3.646801463
[111] 1.449576625 1.516416511 -4.069261782 -0.770114622 -1.805513625
[116] -2.850766503 -3.549117512 0.862307507 1.571139814 -1.259062771
[121] 2.308795767 -0.397630876 -2.478487298 -3.048922102 -1.962405452
[126] -0.707286674 -2.035769213 -1.494414236 1.513349830 -0.287335466
[131] -2.679462744 -1.867884860 -1.340594682 -2.422157228 -3.081290162
[136] 3.030916003 1.440401318 -1.819480162 -3.588853140 1.245387263
[141] -1.746059790 0.354185849 -2.849210621 -3.351835952 -2.028140247
[146] -3.095110718 -2.656122108 2.719794302 1.022357398 -1.051537881
[151] -2.540081151 -1.122607894 1.693988598 0.487745311 -1.894589855
[156] 4.297749244 -2.168753745 -2.498478330 -3.362168995 2.325617792
[161] -1.372154798 -2.501157990 -1.370932954 2.655788016 1.262716128
[166] 1.328395085 -0.669762740 3.093362487 -2.493140455 -2.270311532
[171] -1.526982732 -0.961968245 -2.067512971 3.163287002 -0.527548000
[176] -2.675389419 -2.531998257 -0.462416485 -1.998488479 -2.054475796
[181] 1.395851050 -3.147951487 0.846293070 -4.029681049 2.488915271
[186] -1.934129811 -0.885810025 -2.923969907 -2.169366249 -2.900967697
[191] 3.726396794 -0.160121805 0.294786774 2.239712384 -3.341891276
[196] 0.403008760 -2.427468275 2.999968289 -3.216129599 -1.109208228
[201] 2.763819132 2.294205079 -0.552881017 2.592250198 -2.487951280
[206] -2.926206211 -2.103406419 -2.020267383 -3.671825700 -0.621757184
[211] -0.897199142 0.303594791 -2.246772028 2.139139852 -2.141572297
[216] -2.778848272 -2.378818399 -1.192472520 0.119186495 -1.387094681
[221] 2.234187401 -2.528482433 2.059152096 1.151710620 -2.113370680
[226] -3.936828124 1.741900234 1.036162252 -0.615260642 3.934484839
[231] 1.761989028 0.767813925 2.367604748 -3.349554149 0.895896169
[236] -2.715528091 -1.761948272 -1.669400254 2.104204883 -2.388360870
[241] -1.653755593 1.576019692 -2.400749342 -2.719920374 -2.746219467
[246] -1.026970928 -1.949747502 -3.492983777 -1.955455562 -0.045237981
[251] 2.882938645 -2.480516301 1.917094656 -3.137987321 2.632852405
[256] 0.002447004 -2.711757063 1.770203806 -0.615982456 -0.192056791
[261] -1.963972292 2.975993744 -3.032603748 -2.389887421 -1.764105392
[266] 3.823106542 -1.901663625 2.217791618 1.301191162 -1.771721575
[271] 2.668671609 -2.699486970 -2.287400958 3.194329002 -3.540804336
[276] 1.739017758 3.355355866 -2.889392238 -2.445993676 -1.315351392
[281] 1.838654187 2.382707797 2.233137475 -2.059090045 -2.858582435
[286] -3.320078330 2.086391088 -3.307980300 2.570709658 0.587942208
[291] -2.302585093 1.940488335 -2.874222856 -1.777738228 -1.430789761
[296] -4.297685486 -1.763638594 1.734548701 -1.764922815 -3.457767733
[301] -2.375263318 -1.274502571 -3.822639444 -1.968974089 2.173855866
[306] 2.407674572 -2.093215975 1.761497837 -3.147253081 -1.350348234
[311] 0.838934413 -3.128349798 -2.914152287 -0.911253440 -0.996416776
[316] -1.836589485 -3.373484943 -1.572816729 -1.311632257 -2.195346342
[321] -2.471358837 -0.219761776 -1.958782645 -3.884894338 -3.251657314
[326] 2.505297339 2.469141361 -2.792464952 -4.241221758 2.284776456
[331] -3.689679774 -3.009425601 2.037579944 -1.932678080 2.165246462
[336] -1.078633206 -1.579490836 3.628648973 -2.672924399 -2.317800259
[341] -0.652811704 -0.597709736 -2.984001351 0.807528883 -3.087847562
[346] 2.295041713 -0.235203481 3.220718127 -4.334379981 1.953087190
[351] -2.073141429 -3.511570439 -1.553010321 1.666394639 1.005754765
[356] -1.107299917 0.141256477 -2.427128428 -0.948426602 -2.098094430
[361] -0.241180239 -1.378365875 -2.396566156 -4.513502997 -0.410211354
[366] -2.502622656 -3.368246282 0.052526034 -3.328690691 -2.328518475
[371] 1.306468922 -4.703886159 -1.492143885 -3.393526875 -2.792138581
[376] 1.302428940 -1.154135569 -0.272307535 2.195329449 -1.626736677
[381] -2.673793712 2.549624842 -0.997121250 4.218342320 -2.708450281
[386] -1.144855200 -2.380330442 -1.541358792 -1.239254618 -1.079309786
[391] -0.466186922 0.224574527 -2.991142821 1.895356431 2.098231401
[396] -2.541477001 -1.992016917 -3.061017740 -2.294220177 1.072952542
[401] -3.278625829 2.299233483 0.241737605 -1.972274658 0.894732004
[406] -1.075608907 -1.706003880 -3.601234944 2.745712007 -3.600502343
[411] 1.997025499 -1.239427285 -1.505618584 -2.904247583 0.209020286
[416] -0.627134746 1.307508155 -2.958821920 0.866499467 2.934441805
[421] -2.031261087 2.917673430 -0.978751413 -1.726927241 -2.538813884
[426] -2.786768786 -0.126413925 2.604332779 -1.234775713 -2.201021778
[431] 3.255809309 -0.830778395 -0.257489167 -3.119709453 1.745917954
[436] -2.874577152 -2.400418453 -0.816943258 0.866823138 -2.589867245
[441] -4.342036986 -0.607850606 -0.583790660 -2.441042886 -2.162475385
[446] -1.104217971 1.540908505 0.969065329 -1.786175091 -2.226272410
[451] -5.064036071 1.627311229 -3.409431187 -1.475081860 -3.228673667
[456] -3.295486927 -1.043187043 -1.046570275 -2.352616027 3.117821579
[461] -2.523980174 -3.337940932 -3.109573090 -2.273997636 -1.206776732
[466] 2.002005538 2.822081021 -1.650989340 -2.635962125 -1.210024412
[471] -3.338786122 2.412371799 -3.979231755 -0.472310288 -3.191017497
[476] 0.833083021 -2.301785413 -2.800823601 -1.996056733 -2.999539512
bwplot (log_crime_rate) #transformerad plot #svar: Vi kan säga att enligt beskrivningen ser vi att crime_rate variabeln har medelvärdet är ca 3.67 och histogram kan vi ta reda på en del outlier som påverkar vår medelvärdet. Generellt kan vi säga Boston 1970 har low kriminaliteten.
💪 Uppgift 1.2
Distrikten tillhör olika stadsdelar som anges i den kategoriska variabeln town? Det finns 88 olika sådana stadsdelar (towns).
Skiljer sig brottsligheten åt mellan de olika stadsdelarna? Undersök stadsdelarna Boston East Boston, Boston Downtown, Cambridge, samt ytterligare en stadsdel som ni själva väljer. Besvara frågan med hjälp av lämpligt valda figurer och statistiska mått.
Innan ni påbörjar analysen, skapa en ny data frame som enbart innehåller de stadsdelar som ni vill jämföra. Det kan göras till exempel med funktionen filter().
Skriv svaret här.
suppressMessages (library (dplyr))<- filter (Boston_census, town == "Boston East Boston" | town == "Boston Downtown" | town == "Cambridge" | town == "Newton" )
town longitude latitude median_home_value crime_rate
1 Boston East Boston -71.0215 42.2270 12.3 7.99248
2 Cambridge -71.0480 42.2222 13.4 3.32105
3 Boston Downtown -71.0427 42.2090 27.9 11.95110
4 Cambridge -71.0662 42.2162 17.0 1.41385
5 Boston East Boston -71.0200 42.2205 9.7 11.57790
6 Cambridge -71.0645 42.2150 19.4 2.14918
7 Cambridge -71.0690 42.2285 41.3 1.22358
8 Newton -71.1208 42.2183 30.1 0.61470
9 Cambridge -71.0519 42.2230 15.6 4.09740
10 Boston East Boston -71.0195 42.2255 10.2 14.33370
11 Cambridge -71.0620 42.2236 21.5 1.65660
12 Boston East Boston -71.0410 42.2290 10.9 15.87440
13 Newton -71.1320 42.2142 29.0 0.44791
14 Cambridge -71.0810 42.2368 17.4 1.20742
15 Boston Downtown -71.0455 42.2060 27.5 14.43830
16 Boston Downtown -71.0487 42.2048 17.2 14.05070
17 Newton -71.1285 42.1930 31.7 0.46296
18 Cambridge -71.0555 42.2222 14.6 2.36862
19 Boston Downtown -71.0390 42.2198 11.9 20.71620
20 Cambridge -71.0650 42.2223 23.3 1.42502
21 Cambridge -71.0510 42.2205 11.8 2.77974
22 Cambridge -71.0680 42.2150 15.6 3.53501
23 Newton -71.1491 42.2030 31.5 0.51183
24 Newton -71.1215 42.2025 37.6 0.38214
25 Cambridge -71.0567 42.2240 17.8 2.33099
26 Newton -71.1160 42.1947 31.6 0.41238
27 Cambridge -71.0670 42.2245 24.3 1.34284
28 Cambridge -71.0622 42.2205 19.6 1.49632
29 Boston East Boston -71.0245 42.2235 8.8 20.08490
30 Newton -71.1400 42.1946 41.7 0.57529
31 Newton -71.1300 42.1880 24.3 0.53700
32 Newton -71.1210 42.2166 21.7 0.40771
33 Cambridge -71.0590 42.2170 15.3 1.12658
34 Boston Downtown -71.0462 42.2075 15.0 51.13580
35 Cambridge -71.0590 42.2210 15.6 2.15505
36 Cambridge -71.0775 42.2351 22.3 2.44953
37 Newton -71.1100 42.2137 26.7 0.35809
38 Boston East Boston -71.0230 42.2287 11.3 9.18702
39 Boston East Boston -71.0228 42.2231 10.5 24.39380
40 Boston Downtown -71.0451 42.2015 16.3 28.65580
41 Cambridge -71.0866 42.2345 23.8 1.80028
42 Boston East Boston -71.0109 42.2300 15.1 6.96215
43 Cambridge -71.0792 42.2390 19.1 2.31390
44 Newton -71.1485 42.2110 25.1 0.52058
45 Cambridge -71.0918 42.2265 22.7 2.24236
46 Boston East Boston -71.0035 42.2330 23.2 5.29305
47 Cambridge -71.0580 42.2230 15.4 2.73397
48 Newton -71.1380 42.2141 24.0 0.33045
49 Newton -71.1100 42.2060 44.8 0.31533
50 Boston East Boston -71.0168 42.2281 11.5 8.15174
51 Cambridge -71.0820 42.2295 25.0 2.92400
52 Cambridge -71.0700 42.2214 27.0 1.27346
53 Cambridge -71.0667 42.2200 13.1 2.44668
54 Boston Downtown -71.0470 42.2030 17.9 18.81100
55 Newton -71.1190 42.1807 31.5 0.44178
56 Cambridge -71.0540 42.2190 13.8 2.37934
57 Newton -71.1340 42.2040 48.3 0.33147
58 Boston East Boston -71.0215 42.2248 7.4 22.59710
59 Boston Downtown -71.0437 42.2050 17.2 7.40389
60 Boston East Boston -71.0235 42.2213 7.2 16.81180
61 Newton -71.1100 42.1850 46.7 0.29819
62 Newton -71.1250 42.2134 27.5 0.62356
63 Cambridge -71.0740 42.2331 23.8 2.30040
zoned_25k_p indust_p borders_charles NOx n_rooms_avg before_1940_p
1 0 18.10 0 0.700 5.520 100.0
2 0 19.58 1 0.871 5.403 100.0
3 0 18.10 0 0.659 5.608 100.0
4 0 19.58 1 0.871 6.129 96.0
5 0 18.10 0 0.700 5.036 97.0
6 0 19.58 0 0.871 5.709 98.5
7 0 19.58 0 0.605 6.943 97.4
8 0 6.20 0 0.507 6.618 80.8
9 0 19.58 0 0.871 5.468 100.0
10 0 18.10 0 0.700 4.880 100.0
11 0 19.58 0 0.871 6.122 97.3
12 0 18.10 0 0.671 6.545 99.1
13 0 6.20 1 0.507 6.726 66.5
14 0 19.58 0 0.605 5.875 94.6
15 0 18.10 0 0.597 6.852 100.0
16 0 18.10 0 0.597 6.657 100.0
17 0 6.20 0 0.504 7.412 76.9
18 0 19.58 0 0.871 4.926 95.7
19 0 18.10 0 0.659 4.138 100.0
20 0 19.58 0 0.871 6.510 100.0
21 0 19.58 0 0.871 4.903 97.8
22 0 19.58 1 0.871 6.152 82.6
23 0 6.20 0 0.507 7.358 71.6
24 0 6.20 0 0.504 8.040 86.5
25 0 19.58 0 0.871 5.186 93.8
26 0 6.20 0 0.504 7.163 79.9
27 0 19.58 0 0.605 6.066 100.0
28 0 19.58 0 0.871 5.404 100.0
29 0 18.10 0 0.700 4.368 91.2
30 0 6.20 0 0.507 8.337 73.3
31 0 6.20 0 0.504 5.981 68.1
32 0 6.20 1 0.507 6.164 91.3
33 0 19.58 1 0.871 5.012 88.0
34 0 18.10 0 0.597 5.757 100.0
35 0 19.58 0 0.871 5.628 100.0
36 0 19.58 0 0.605 6.402 95.2
37 0 6.20 1 0.507 6.951 88.5
38 0 18.10 0 0.700 5.536 100.0
39 0 18.10 0 0.700 4.652 100.0
40 0 18.10 0 0.597 5.155 100.0
41 0 19.58 0 0.605 5.877 79.2
42 0 18.10 0 0.700 5.713 97.0
43 0 19.58 0 0.605 5.880 97.3
44 0 6.20 1 0.507 6.631 76.5
45 0 19.58 0 0.605 5.854 91.8
46 0 18.10 0 0.700 6.051 82.5
47 0 19.58 0 0.871 5.597 94.9
48 0 6.20 0 0.507 6.086 61.5
49 0 6.20 0 0.504 8.266 78.3
50 0 18.10 0 0.700 5.390 98.9
51 0 19.58 0 0.605 6.101 93.0
52 0 19.58 1 0.605 6.250 92.6
53 0 19.58 0 0.871 5.272 94.0
54 0 18.10 0 0.597 4.628 100.0
55 0 6.20 0 0.504 6.552 21.4
56 0 19.58 0 0.871 6.130 100.0
57 0 6.20 0 0.507 8.247 70.4
58 0 18.10 0 0.700 5.000 89.5
59 0 18.10 0 0.597 5.617 97.9
60 0 18.10 0 0.700 5.277 98.1
61 0 6.20 0 0.504 7.686 17.0
62 0 6.20 1 0.507 6.879 77.7
63 0 19.58 0 0.605 6.319 96.1
employ_dist radial_access tax_rate pupil_teacher_ratio lower_stat_pct
1 1.5331 24 666 20.2 24.56
2 1.3216 5 403 14.7 26.82
3 1.2852 24 666 20.2 12.13
4 1.7494 5 403 14.7 15.12
5 1.7700 24 666 20.2 25.68
6 1.6232 5 403 14.7 15.79
7 1.8773 5 403 14.7 4.59
8 3.2721 8 307 17.4 7.60
9 1.4118 5 403 14.7 26.42
10 1.5895 24 666 20.2 30.62
11 1.6180 5 403 14.7 14.10
12 1.5192 24 666 20.2 21.08
13 3.6519 8 307 17.4 8.05
14 2.4259 5 403 14.7 14.43
15 1.4655 24 666 20.2 19.78
16 1.5275 24 666 20.2 21.22
17 3.6715 8 307 17.4 5.25
18 1.4608 5 403 14.7 29.53
19 1.1781 24 666 20.2 23.34
20 1.7659 5 403 14.7 7.39
21 1.3459 5 403 14.7 29.29
22 1.7455 5 403 14.7 15.02
23 4.1480 8 307 17.4 4.73
24 3.2157 8 307 17.4 3.13
25 1.5296 5 403 14.7 28.32
26 3.2157 8 307 17.4 6.36
27 1.7573 5 403 14.7 6.43
28 1.5916 5 403 14.7 13.28
29 1.4395 24 666 20.2 30.63
30 3.8384 8 307 17.4 2.47
31 3.6715 8 307 17.4 11.65
32 3.0480 8 307 17.4 21.46
33 1.6102 5 403 14.7 12.12
34 1.4130 24 666 20.2 10.11
35 1.5166 5 403 14.7 16.65
36 2.2625 5 403 14.7 11.32
37 2.8617 8 307 17.4 9.71
38 1.5804 24 666 20.2 23.60
39 1.4672 24 666 20.2 28.28
40 1.5894 24 666 20.2 20.08
41 2.4259 5 403 14.7 12.14
42 1.9265 24 666 20.2 17.11
43 2.3887 5 403 14.7 12.03
44 4.1480 8 307 17.4 9.54
45 2.4220 5 403 14.7 11.64
46 2.1678 24 666 20.2 18.76
47 1.5257 5 403 14.7 21.45
48 3.6519 8 307 17.4 10.88
49 2.8944 8 307 17.4 4.14
50 1.7281 24 666 20.2 20.85
51 2.2834 5 403 14.7 9.81
52 1.7984 5 403 14.7 5.50
53 1.7364 5 403 14.7 16.14
54 1.5539 24 666 20.2 34.37
55 3.3751 8 307 17.4 3.76
56 1.4191 5 403 14.7 27.80
57 3.6519 8 307 17.4 3.95
58 1.5184 24 666 20.2 31.99
59 1.4547 24 666 20.2 26.40
60 1.4261 24 666 20.2 30.81
61 3.3751 8 307 17.4 3.92
62 3.2721 8 307 17.4 9.93
63 2.1000 5 403 14.7 11.10
dist_fenway_park
1 9473.040
2 7084.501
3 7843.377
4 5835.269
5 9729.686
6 5983.767
7 5292.740
8 5311.258
9 6734.679
10 9696.166
11 5921.595
12 7563.394
13 6137.994
14 4350.347
15 7675.541
16 7441.357
17 6533.139
18 6459.483
19 7935.629
20 5742.059
21 6869.593
22 5754.337
23 7746.720
24 5848.785
25 6318.313
26 5855.101
27 5542.101
28 5991.314
29 9237.024
30 7250.903
31 6774.526
32 5375.525
33 6322.249
34 7581.210
35 6216.274
36 4580.270
37 4992.298
38 9296.364
39 9410.310
40 7816.928
41 4209.231
42 10493.650
43 4366.947
44 7506.759
45 4369.162
46 11213.265
47 6241.371
48 6581.485
49 5259.033
50 9924.656
51 4548.367
52 5437.980
53 5691.870
54 7624.081
55 6446.573
56 6660.058
57 6563.712
58 9509.525
59 7850.651
60 9372.921
61 5991.509
62 5704.143
63 4840.659
boxplot (crime_rate ~ town, data = Boston_census_new)histogram (~ crime_rate | town, data = Boston_census_new,type = "percent" )favstats (~ crime_rate | town, data = Boston_census_new)
town min Q1 median Q3 max mean
1 Boston Downtown 7.40389 13.525800 16.62465 22.701100 51.13580 20.8953488
2 Boston East Boston 5.29305 8.111925 12.95580 17.630075 24.39380 13.6050033
3 Cambridge 1.12658 1.442845 2.27138 2.448818 4.09740 2.1728142
4 Newton 0.29819 0.358090 0.44178 0.520580 0.62356 0.4453747
sd n missing
1 13.7822237 8 0
2 6.4193740 12 0
3 0.7706757 26 0
4 0.1045755 17 0
# Enligt histogram som vi skapade kan vi tydligt se att Boston Downtown har högre crime_rate än de resten grupperna. I och med enligt deskriptiv data som vi fick i figuren 3 där har Boston Downtown högst medelvärdet jämfört med de resten grupperna dvs vi kan dra ett beslut att Boston Downtown har en högre brottslighet än de resten grupperna.
💪 Uppgift 1.3
Vilka två variabler i datasetet Boston_census_data korrelerar mest med brottslighet? Beskriv sambandet mellan brottslighet och var och en av dessa två variabler.
Kom ihåg att korrelation mäter det linjära sambandet mellan numeriska variabler .
Skriv svaret här.
suppressMessages (library (mosaic))<- Boston_census_data[, c ("longitude" ,"latitude" ,"median_home_value" ,"crime_rate" ,"zoned_25k_p" ,"indust_p" ,"NOx" ,"n_rooms_avg" ,"before_1940_p" ,"employ_dist" ,"pupil_teacher_ratio" ,"lower_stat_pct" ,"dist_fenway_park" )]head (Boston_census_data_13_variables)
longitude latitude median_home_value crime_rate zoned_25k_p indust_p NOx
1 -71.0677 42.2335 17.4 0.32264 0 21.89 0.624
2 -70.9650 42.1503 18.7 0.06151 0 5.19 0.515
3 -71.0215 42.2270 12.3 7.99248 0 18.10 0.700
4 -71.0511 42.1879 13.3 6.39312 0 18.10 0.584
5 -71.0455 42.1768 14.4 9.51363 0 18.10 0.713
6 -71.1095 42.3008 21.7 0.15876 0 10.81 0.413
n_rooms_avg before_1940_p employ_dist pupil_teacher_ratio lower_stat_pct
1 5.942 93.5 1.9669 21.2 16.90
2 5.968 58.5 4.8122 20.2 9.29
3 5.520 100.0 1.5331 20.2 24.56
4 6.162 97.4 2.2060 20.2 24.10
5 6.728 94.1 2.4961 20.2 18.71
6 5.961 17.5 5.2873 19.2 9.88
dist_fenway_park
1 5238.465
2 16343.799
3 9473.040
4 7692.103
5 8409.662
6 2137.660
<- cor (Boston_census_data_13_variables)#vi ser att variabel lower_stat_pct har en stark positiv korrelation ungefär 0,35 till crime_rate, variabel median_home_value har dock en stark negativ korrelation ungeför -0.45 till crime_rate. round (correlation_matrix_Boston, 3 )
longitude latitude median_home_value crime_rate zoned_25k_p
longitude 1.000 0.140 -0.326 0.056 -0.168
latitude 0.140 1.000 0.015 -0.092 -0.123
median_home_value -0.326 0.015 1.000 -0.450 0.395
crime_rate 0.056 -0.092 -0.450 1.000 -0.196
zoned_25k_p -0.168 -0.123 0.395 -0.196 1.000
indust_p 0.035 -0.063 -0.600 0.403 -0.522
NOx 0.138 -0.085 -0.524 0.415 -0.506
n_rooms_avg -0.227 -0.069 0.680 -0.213 0.299
before_1940_p 0.183 0.060 -0.488 0.350 -0.559
employ_dist 0.011 -0.069 0.368 -0.378 0.672
pupil_teacher_ratio 0.295 -0.002 -0.515 0.283 -0.375
lower_stat_pct 0.172 0.034 -0.765 0.464 -0.424
dist_fenway_park 0.435 -0.200 -0.003 -0.123 0.400
indust_p NOx n_rooms_avg before_1940_p employ_dist
longitude 0.035 0.138 -0.227 0.183 0.011
latitude -0.063 -0.085 -0.069 0.060 -0.069
median_home_value -0.600 -0.524 0.680 -0.488 0.368
crime_rate 0.403 0.415 -0.213 0.350 -0.378
zoned_25k_p -0.522 -0.506 0.299 -0.559 0.672
indust_p 1.000 0.761 -0.407 0.632 -0.706
NOx 0.761 1.000 -0.315 0.725 -0.766
n_rooms_avg -0.407 -0.315 1.000 -0.256 0.242
before_1940_p 0.632 0.725 -0.256 1.000 -0.744
employ_dist -0.706 -0.766 0.242 -0.744 1.000
pupil_teacher_ratio 0.382 0.178 -0.289 0.264 -0.237
lower_stat_pct 0.640 0.615 -0.607 0.633 -0.544
dist_fenway_park -0.394 -0.325 0.020 -0.304 0.686
pupil_teacher_ratio lower_stat_pct dist_fenway_park
longitude 0.295 0.172 0.435
latitude -0.002 0.034 -0.200
median_home_value -0.515 -0.765 -0.003
crime_rate 0.283 0.464 -0.123
zoned_25k_p -0.375 -0.424 0.400
indust_p 0.382 0.640 -0.394
NOx 0.178 0.615 -0.325
n_rooms_avg -0.289 -0.607 0.020
before_1940_p 0.264 0.633 -0.304
employ_dist -0.237 -0.544 0.686
pupil_teacher_ratio 1.000 0.364 0.043
lower_stat_pct 0.364 1.000 -0.161
dist_fenway_park 0.043 -0.161 1.000
suppressMessages (library (corrplot))corrplot (correlation_matrix_Boston)cor (lower_stat_pct ~ Crime_rate, data = Boston_census)cor (median_home_value ~ Crime_rate, data = Boston_census)
2. Fastighetsskatt i Boston
I detta avsnitt ska ni analysera fastighetsskatten i Boston med hjälp av variabeln tax_rate.
💪 Uppgift 2.1
Vad kan man generellt säga om fastighetsskatten i distrikten? Använd lämpliga figurer och mått för att beskriva fördelningen.
Skriv svaret här.
histogram (~ tax_rate, data = Boston_census_data, main = "Fastighetsskatten i Boston" , type = "count" ,col= "navyblue" )favstats (~ tax_rate, data = Boston_census)
min Q1 median Q3 max mean sd n missing
187 280.75 330 666 711 409.3271 168.5777 480 0
densityplot (~ tax_rate, data = Boston_census_data, col = "red" )
💪 Uppgift 2.2
Låt oss skapa en ny variabel cat_tax som anger om ett distrikt betalar låg (low), medel (medium), eller hög (high) fastighetsskatt. Vi definerar skattekategorierna enligt
low: tax_rate \(\leq\) 250,medium: 250 \(<\) tax_rate \(\leq\) 400,high: tax_rate \(>\) 400.
Följande kod skapar och lägger till variabeln cat_tax i Boston_census_data
$ cat_tax <- cut (Boston_census_data$ tax_rate, breaks= c (0 , 250 , 400 , 800 ),labels= c ('Low' , 'Medium' , 'High' ))
Finns det ett samband mellan vilken skattekategori ett distrikt tillhör och dess angränsning till Charles River? Förklara med hjälp av lämplig tabell och figur.
Skriv svaret här.
tally (cat_tax ~ borders_charles, data = Boston_census_data, margins= TRUE , format= "percent" )
borders_charles
cat_tax 0 1
Low 13.30377 10.34483
Medium 47.00665 55.17241
High 39.68958 34.48276
Total 100.00000 100.00000
💪 Uppgift 2.3
Hur många procent av alla distrikt i vår data ligger i angränsning till Charles River och tillhör en hög skattekategori? Hur stor andel av distrikten med hög skatt ligger inte i angränsning till Charles River?
Skriv svaret här.
tally (borders_charles ~ cat_tax, data = Boston_census_data, margins= TRUE , format= "percent" )
cat_tax
borders_charles Low Medium High
0 95.238095 92.982456 94.708995
1 4.761905 7.017544 5.291005
Total 100.000000 100.000000 100.000000
💪 Uppgift 2.4
Vilka två variabler i datasetet Boston_census_data korrelerar starkast med tax_rate? Beskriv det parvisa sambandet mellan tax_rate och var och en av dessa två variabler. Vad kan vi säga om kausalitet för vart och ett av sambanden?
Kom ihåg att korrelation är ett mått på linjära samband mellan numeriska variabler .
Skriv svaret här.
suppressMessages (library (mosaic))#vi ser att variabel lower_stat_pct har en stark positiv korrelation ungefär 0,35 till crime_rate, variabel median_home_value har dock en stark negativ korrelation ungeför -0.45 till crime_rate. <- Boston_census_data[, c ("longitude" ,"latitude" ,"median_home_value" ,"crime_rate" ,"zoned_25k_p" ,"indust_p" ,"NOx" ,"n_rooms_avg" ,"before_1940_p" ,"employ_dist" ,"radial_access" ,"tax_rate" ,"pupil_teacher_ratio" ,"lower_stat_pct" ,"dist_fenway_park" )]head (Boston_census_data_15_variables)
longitude latitude median_home_value crime_rate zoned_25k_p indust_p NOx
1 -71.0677 42.2335 17.4 0.32264 0 21.89 0.624
2 -70.9650 42.1503 18.7 0.06151 0 5.19 0.515
3 -71.0215 42.2270 12.3 7.99248 0 18.10 0.700
4 -71.0511 42.1879 13.3 6.39312 0 18.10 0.584
5 -71.0455 42.1768 14.4 9.51363 0 18.10 0.713
6 -71.1095 42.3008 21.7 0.15876 0 10.81 0.413
n_rooms_avg before_1940_p employ_dist radial_access tax_rate
1 5.942 93.5 1.9669 4 437
2 5.968 58.5 4.8122 5 224
3 5.520 100.0 1.5331 24 666
4 6.162 97.4 2.2060 24 666
5 6.728 94.1 2.4961 24 666
6 5.961 17.5 5.2873 4 305
pupil_teacher_ratio lower_stat_pct dist_fenway_park
1 21.2 16.90 5238.465
2 20.2 9.29 16343.799
3 20.2 24.56 9473.040
4 20.2 24.10 7692.103
5 20.2 18.71 8409.662
6 19.2 9.88 2137.660
<- cor (Boston_census_data_15_variables)round (correlation_matrix_Boston, 3 )
longitude latitude median_home_value crime_rate zoned_25k_p
longitude 1.000 0.140 -0.326 0.056 -0.168
latitude 0.140 1.000 0.015 -0.092 -0.123
median_home_value -0.326 0.015 1.000 -0.450 0.395
crime_rate 0.056 -0.092 -0.450 1.000 -0.196
zoned_25k_p -0.168 -0.123 0.395 -0.196 1.000
indust_p 0.035 -0.063 -0.600 0.403 -0.522
NOx 0.138 -0.085 -0.524 0.415 -0.506
n_rooms_avg -0.227 -0.069 0.680 -0.213 0.299
before_1940_p 0.183 0.060 -0.488 0.350 -0.559
employ_dist 0.011 -0.069 0.368 -0.378 0.672
radial_access 0.015 -0.226 -0.479 0.623 -0.303
tax_rate 0.031 -0.189 -0.579 0.580 -0.300
pupil_teacher_ratio 0.295 -0.002 -0.515 0.283 -0.375
lower_stat_pct 0.172 0.034 -0.765 0.464 -0.424
dist_fenway_park 0.435 -0.200 -0.003 -0.123 0.400
indust_p NOx n_rooms_avg before_1940_p employ_dist
longitude 0.035 0.138 -0.227 0.183 0.011
latitude -0.063 -0.085 -0.069 0.060 -0.069
median_home_value -0.600 -0.524 0.680 -0.488 0.368
crime_rate 0.403 0.415 -0.213 0.350 -0.378
zoned_25k_p -0.522 -0.506 0.299 -0.559 0.672
indust_p 1.000 0.761 -0.407 0.632 -0.706
NOx 0.761 1.000 -0.315 0.725 -0.766
n_rooms_avg -0.407 -0.315 1.000 -0.256 0.242
before_1940_p 0.632 0.725 -0.256 1.000 -0.744
employ_dist -0.706 -0.766 0.242 -0.744 1.000
radial_access 0.592 0.608 -0.190 0.447 -0.486
tax_rate 0.717 0.666 -0.278 0.499 -0.529
pupil_teacher_ratio 0.382 0.178 -0.289 0.264 -0.237
lower_stat_pct 0.640 0.615 -0.607 0.633 -0.544
dist_fenway_park -0.394 -0.325 0.020 -0.304 0.686
radial_access tax_rate pupil_teacher_ratio lower_stat_pct
longitude 0.015 0.031 0.295 0.172
latitude -0.226 -0.189 -0.002 0.034
median_home_value -0.479 -0.579 -0.515 -0.765
crime_rate 0.623 0.580 0.283 0.464
zoned_25k_p -0.303 -0.300 -0.375 -0.424
indust_p 0.592 0.717 0.382 0.640
NOx 0.608 0.666 0.178 0.615
n_rooms_avg -0.190 -0.278 -0.289 -0.607
before_1940_p 0.447 0.499 0.264 0.633
employ_dist -0.486 -0.529 -0.237 -0.544
radial_access 1.000 0.910 0.453 0.513
tax_rate 0.910 1.000 0.455 0.572
pupil_teacher_ratio 0.453 0.455 1.000 0.364
lower_stat_pct 0.513 0.572 0.364 1.000
dist_fenway_park -0.206 -0.233 0.043 -0.161
dist_fenway_park
longitude 0.435
latitude -0.200
median_home_value -0.003
crime_rate -0.123
zoned_25k_p 0.400
indust_p -0.394
NOx -0.325
n_rooms_avg 0.020
before_1940_p -0.304
employ_dist 0.686
radial_access -0.206
tax_rate -0.233
pupil_teacher_ratio 0.043
lower_stat_pct -0.161
dist_fenway_park 1.000
corrplot (correlation_matrix_Boston)cor (radial_access ~ tax_rate, data = Boston_census)cor (indust_p ~ tax_rate, data = Boston_census)
3. Avstånd till Fenway park
I detta avsnitt ska ni undersöka variabeln dist_fenway_park, som mäter avståndet mellan ett distrikt och Fenway park (stadion där basebollslaget Boston Red Sox spelar sina hemmamatcher).
Vi kan visualisera Fenway park och distrikten på en karta med hjälp av R-paketet leaflet. Följande kod visar platsen för Fenway park och distrikten för observationerna 30 och 45.
library (leaflet) # Install if not available <- c (42.346462 , - 71.097250 ) # latitude and longitude for Fenway_park <- leaflet () %>% addTiles () %>% addMarkers (lat = fenway_park_lat_long[1 ], lng = fenway_park_lat_long[2 ], popup= "Fenway park" ) %>% addMarkers (lat = Boston_census_data$ latitude[30 ], lng = Boston_census_data$ longitude[30 ], popup= "Observation 30" ) %>% addMarkers (lat = Boston_census_data$ latitude[45 ], lng = Boston_census_data$ longitude[45 ], popup= "Observation 45" ) # Show interactive map
💪 Uppgift 3.1
Vilket distrikt i vår data har längst respektive kortast avstånd till Fenway park? Markera ut dessa distrikt i en interaktiv karta tillsammans med Fenway park.
Skriv svaret här.
<- Boston_census_data %>% arrange (dist_fenway_park)head (dist_fenway_sorted)<- Boston_census_data %>% arrange (desc (dist_fenway_park))head (dist_fenway_sorted_hightolow)library (leaflet) # Install if not available <- c (42.346462 , - 71.097250 ) # latitude and longitude for Fenway_park <- leaflet () %>% addTiles () %>% addMarkers (lat = fenway_park_lat_long[1 ], lng = fenway_park_lat_long[2 ], popup= "Fenway park" ) %>% addMarkers (lat = dist_fenway_sorted$ latitude[1 ], lng = dist_fenway_sorted$ longitude[1 ], popup= "kortast" ) %>% addMarkers (lat = dist_fenway_sorted_hightolow$ latitude[1 ], lng = dist_fenway_sorted_hightolow$ longitude[1 ], popup= "Observation längst" )
💪 Uppgift 3.2
Finns det ett samband mellan dist_fenway_park och crime_rate?
Skriv svaret här.
cor (dist_fenway_park ~ crime_rate, data = Boston_census_data)
4. Enkel linjär regression
I detta avsnitt ska ni anpassa och tolka enkla linjära regressionsmodeller.
💪 Uppgift 4.1
Anpassa en linjär regression med responsvariabeln NOx och den förklarande variabeln employ_dist. Rita den anpassade regressionslinjen tillsammans med data i en lämplig figur. Beskriv resultaten och tolka modellen. Utför en modellvalidering via en residualanalys och kommentera modellens lämplighet. Om modellen inte anses lämplig, vilka antaganden har inte varit uppfyllda?
Skriv svaret här.
#Vi anpassar en enkel linjär regression med responsvariabel utandningsvolymen (NOx) och förklarande variabel längd (employdist) med hjälp av funktionen lm() som står för linear model och sparar resultatet i en variabel vi döper till lm_NOx_vs_employdist. <- lm (NOx ~ employ_dist, data = Boston_census)
Call:
lm(formula = NOx ~ employ_dist, data = Boston_census)
Coefficients:
(Intercept) employ_dist
0.71761 -0.04264
#En sådan funktion är summary() som skriver ut resultaten från regressionen i ett snyggt format. summary (lm_NOx_vs_employdist)
Call:
lm(formula = NOx ~ employ_dist, data = Boston_census)
Residuals:
Min 1Q Median 3Q Max
-0.12358 -0.05352 -0.01220 0.04348 0.22868
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.717614 0.007109 100.95 <2e-16 ***
employ_dist -0.042641 0.001639 -26.02 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.07526 on 478 degrees of freedom
Multiple R-squared: 0.5861, Adjusted R-squared: 0.5852
F-statistic: 676.9 on 1 and 478 DF, p-value: < 2.2e-16
plot (NOx ~ employ_dist, data = Boston_census, col = "cornflowerblue" , ylim = c (0 , 1 ))<- predict (lm_NOx_vs_employdist)head (y_hat)
1 2 3 4 5 6
0.6337437 0.5124177 0.6522413 0.6235483 0.6111782 0.4921591
lines (Boston_census$ employ_dist, y_hat, type = "p" , col = "lightcoral" )abline (lm_NOx_vs_employdist, col = "lightcoral" )legend (x = "topleft" , pch = c (1 , 1 , NA ), lty = c (NA , NA , 1 ), col = c ("cornflowerblue" , "lightcoral" , "lightcoral" ), legend= c ("Data" , "Predicted" , "Fitted line" ))# residualer <- residuals (lm_NOx_vs_employdist)head (resid)
1 2 3 4 5 6
-0.009743748 0.002582251 0.047758653 -0.039548322 0.101821788 -0.079159083
# residualanalys plot (Boston_census$ employ_dist, resid, xlab= "employ_dist" , ylab= 'Residuals' , col = "cornflowerblue" ) qqnorm (resid, col = "cornflowerblue" ) # Create normal probability plot for residuals qqline (resid, col = "red" ) # Add a straight line to normal probability plot # Den är inte så slumpmässig, inte normalfördelad. # enkel linjär regression
💪 Uppgift 4.2
Använd modellen i Uppgift 4.1 för att prediktera koncentration av kväveoxider för observation 10, där employ_dist=10.5857. Beräkna vad residualen blir för denna observation.
Skriv svaret här.
<- data.frame (employ_dist = c (10.5857 ))predict (lm_NOx_vs_employdist, newdata = new_x)
💪 Uppgift 4.3
Transformera variablerna i Uppgift 4.1 (avgör själv vilken eller vilka av de två som behöver transformeras). Ett förslag är att använda Tukeys cirkel för att hitta lämpliga transformationer. Anpassa en ny linjär regression med de transformerade variablerna. Utför en modellvalidering (efter transformation) via en residualanalys och kommentera modellens lämplighet jämfört med modellen i Uppgift 4.1.
Skriv svaret här.
plot (NOx ~ employ_dist, data = Boston_census,col = "cornflowerblue" ) # Starting point: Both y and x untransformed (Step 2 in the ladder of powers) plot (sqrt (NOx) ~ sqrt (employ_dist), data = Boston_census, col = "cornflowerblue" )# y down and x down in the ladder of powers plot (log (NOx) ~ log (employ_dist), data = Boston_census, col = "cornflowerblue" )# y and x down two steps <- lm (log (NOx) ~ log (employ_dist), data = Boston_census)summary (lm_logNOx_vs_logemploy_dist)
Call:
lm(formula = log(NOx) ~ log(employ_dist), data = Boston_census)
Residuals:
Min 1Q Median 3Q Max
-0.19152 -0.07828 -0.01370 0.06108 0.28496
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.218492 0.011524 -18.96 <2e-16 ***
log(employ_dist) -0.327303 0.008832 -37.06 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.103 on 478 degrees of freedom
Multiple R-squared: 0.7418, Adjusted R-squared: 0.7413
F-statistic: 1373 on 1 and 478 DF, p-value: < 2.2e-16
<- predict (lm_logNOx_vs_logemploy_dist) # log scale prediction <- exp (logy_hat) # original scale prediction head (logy_hat)
1 2 3 4 5 6
-0.4398989 -0.7327355 -0.3583458 -0.4774478 -0.5178857 -0.7635522
1 2 3 4 5 6
0.6441015 0.4805925 0.6988314 0.6203647 0.5957789 0.4660081
plot (NOx ~ employ_dist, data = Boston_census, col = "cornflowerblue" , ylim = c (0 , 7 )) # Data on original scale lines (Boston_census$ employ_dist, y_hat, type = "p" , col = "lightcoral" )abline (lm_logNOx_vs_logemploy_dist, col = "lightcoral" )legend (x = "topleft" , pch = c (0 ,5 , 0 ,5 , NA ), lty = c (NA , NA , 0 ,5 ), col = c ("cornflowerblue" , "lightcoral" , "lightcoral" ), legend= c ("Data" , "Predicted" , "Fitted line" ))
💪 Uppgift 4.4
Plotta den anpassade regressionen från 4.3 i icke-transformerad skala tillsammans med observationerna (också i icke-transformerad skala) i en lämplig figur.
💪 Uppgift 4.5
Använd modellen i Uppgift 4.3 för att prediktera koncentration av kväveoxider i icke-transformerad skala för observation 10, där employ_dist=10.5857. Beräkna vad residualen blir för denna observation. Kommentera resultaten jämfört med Uppgift 4.2.
Tänk på att ta hänsyn till eventuella transformationer!
5. Multipel linjär regression
I detta avsnitt ska ni studera multipel linjära regression.
💪 Uppgift 5.1
Anpassa en linjär regression med responsvariabel logaritmerad median_home_value samt förklarande variabler lower_stat_pct och dummy-variabeln borders_charles. Tolka koefficienten för borders_charles.
Skriv svaret här.
<- lm (median_home_value ~ lower_stat_pct + borders_charles, data = Boston_census)summary (lm_median_home_value_vs_lower_stat_pct_borders_charles)
Call:
lm(formula = median_home_value ~ lower_stat_pct + borders_charles,
data = Boston_census)
Residuals:
Min 1Q Median 3Q Max
-9.2067 -3.1683 -0.9976 1.6929 21.4235
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 32.36661 0.48051 67.358 <2e-16 ***
lower_stat_pct -0.84435 0.03231 -26.130 <2e-16 ***
borders_charles 2.37058 0.95755 2.476 0.0136 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.998 on 477 degrees of freedom
Multiple R-squared: 0.5912, Adjusted R-squared: 0.5895
F-statistic: 345 on 2 and 477 DF, p-value: < 2.2e-16
<- residuals (lm_median_home_value_vs_lower_stat_pct_borders_charles)head (resid_median_multiplereg)
1 2 3 4 5 6
-0.6971156 -5.8226098 0.6705960 1.2821955 -2.1688443 -2.3244440
plot (lm_median_home_value_vs_lower_stat_pct_borders_charles$ fitted.values, resid_median_multiplereg, xlab= "y_hat" , ylab= 'Residuals' , col = "cornflowerblue" ) qqnorm (resid_median_multiplereg, col = "cornflowerblue" ) # Create normal probability plot for residuals qqline (resid_median_multiplereg, col = "red" ) # Add a straight line to normal
💪 Uppgift 5.2
Ni ska nu utforma en modell som predikterar medianhuspriset median_home_value. Ni får endast använda följande förklaringsvariabler:
before_1940_pcrime_rateradial_accessNOxdist_fenway_park
Ni får själva välja hur många av variablerna som ska ingå i modellen. Ni får göra vilka transformationer ni vill av variablerna, inklusive responsvariabeln.
Pröva er fram metodiskt när ni väljer vilka variabler ni inkluderar i modellen, och när ni bestämmer vilka eventuella transformationer ni använder.
När ni utvärderar olika modeller kan ni förslagsvis börja med att jämföra adjusted R-squared. När ni med hjälp av adjusted R-squared har identifierat två eller tre modeller som ser lovande ut kan ni utvärdera dessa modeller ytterligare i ett andra steg.
I det andra steget ska ni utvärdera hur väl modellerna predikterar data som inte använts för att anpassa modellen. Ni kan välja en av två alternativa metoder :
Dela in ert dataset i träningsdata och testdata. Anpassa modellen med hjälp av träningsdata, och utvärdera sedan på testdata. Ni kan exempelvis använda de första 350 observationerna som träningsdata och de sista 130 observationerna som testdata.
Använd korsvalidering. Det är en något mer krävande metod, men också något bättre. Ni kan exempelvis göra korsvalidering med 4 folds (4-fold cross validation). Dela då upp ert dataset i fyra delar (del 1: observationer 1-120, del 2: observationer 121-240, del 3: observationer 241-360, del 4: observationer 361-480).
Sortera inte observationerna i Boston_census_data slumpmässigt. Ordningen är redan slumpmässig.
Tänk på att ta hänsyn till eventuell transformation av responsvariabeln. Om ni exempelvis har valt transformationen \(\log(y)\) är modellens prediktion av responsvariabeln \(\widehat{\log(y)}\) . Ni måste då transformera den till \(\hat y\) i responsvariabelns originalskala med formeln \(\hat{y}=\exp\left(\widehat{\log(y)}\right)\) . Sedan kan ni räkna ut residualen \(y - \hat y\) .
Skriv svaret här
plot (median_home_value ~ before_1940_p + NOx, data = Boston_census, col = "cornflowerblue" )
💪 Uppgift 5.3
Gör en residualanalys av den valda modellen i Uppgift 5.2.
💪 Uppgift 5.4
Använd modellen i Uppgift 5.2 för att prediktera medianhuspriset för observationerna i datasetet Boston_districts_to_predict (ladda ner ).
Ladda in dataseten Boston_districts_to_predict med följande kod.
# Write your code here load (file = url ("https://github.com/StatisticsSU/SDA1/blob/main/assignments/assignment1/Boston_districts_to_predict.RData?raw=true" ))
Det här datasetet har endast de förklarande variablerna, dvs inte responsvariabeln. När vi rättar era inlämningsuppgifter kommer vi att jämföra era prediktioner med de faktiska medianpriserna (som vi har tillgång till).
Skriv ut dina prediktioner så att vi enkelt kan se dem när vi rättar.
Tänk på att ta hänsyn till eventuella transformationer av förklaringsvariablerna!
